social media fake news essay

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Fake news, disinformation and misinformation in social media: a review

Esma aïmeur.

Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada

Sabrine Amri

Gilles brassard, associated data.

All the data and material are available in the papers cited in the references.

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Introduction

Context and motivation.

Fake news, disinformation and misinformation have become such a scourge that Marcia McNutt, president of the National Academy of Sciences of the United States, is quoted to have said (making an implicit reference to the COVID-19 pandemic) “Misinformation is worse than an epidemic: It spreads at the speed of light throughout the globe and can prove deadly when it reinforces misplaced personal bias against all trustworthy evidence” in a joint statement of the National Academies 1 posted on July 15, 2021. Indeed, although online social networks (OSNs), also called social media, have improved the ease with which real-time information is broadcast; its popularity and its massive use have expanded the spread of fake news by increasing the speed and scope at which it can spread. Fake news may refer to the manipulation of information that can be carried out through the production of false information, or the distortion of true information. However, that does not mean that this problem is only created with social media. A long time ago, there were rumors in the traditional media that Elvis was not dead, 2 that the Earth was flat, 3 that aliens had invaded us, 4 , etc.

Therefore, social media has become nowadays a powerful source for fake news dissemination (Sharma et al. 2019 ; Shu et al. 2017 ). According to Pew Research Center’s analysis of the news use across social media platforms, in 2020, about half of American adults get news on social media at least sometimes, 5 while in 2018, only one-fifth of them say they often get news via social media. 6

Hence, fake news can have a significant impact on society as manipulated and false content is easier to generate and harder to detect (Kumar and Shah 2018 ) and as disinformation actors change their tactics (Kumar and Shah 2018 ; Micallef et al. 2020 ). In 2017, Snow predicted in the MIT Technology Review (Snow 2017 ) that most individuals in mature economies will consume more false than valid information by 2022.

Recent news on the COVID-19 pandemic, which has flooded the web and created panic in many countries, has been reported as fake. 7 For example, holding your breath for ten seconds to one minute is not a self-test for COVID-19 8 (see Fig.  1 ). Similarly, online posts claiming to reveal various “cures” for COVID-19 such as eating boiled garlic or drinking chlorine dioxide (which is an industrial bleach), were verified 9 as fake and in some cases as dangerous and will never cure the infection.

Fake news example about a self-test for COVID-19 source: https://cdn.factcheck.org/UploadedFiles/Screenshot031120_false.jpg , last access date: 26-12-2022

Social media outperformed television as the major news source for young people of the UK and the USA. 10 Moreover, as it is easier to generate and disseminate news online than with traditional media or face to face, large volumes of fake news are produced online for many reasons (Shu et al. 2017 ). Furthermore, it has been reported in a previous study about the spread of online news on Twitter (Vosoughi et al. 2018 ) that the spread of false news online is six times faster than truthful content and that 70% of the users could not distinguish real from fake news (Vosoughi et al. 2018 ) due to the attraction of the novelty of the latter (Bovet and Makse 2019 ). It was determined that falsehood spreads significantly farther, faster, deeper and more broadly than the truth in all categories of information, and the effects are more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information (Vosoughi et al. 2018 ).

Over 1 million tweets were estimated to be related to fake news by the end of the 2016 US presidential election. 11 In 2017, in Germany, a government spokesman affirmed: “We are dealing with a phenomenon of a dimension that we have not seen before,” referring to an unprecedented spread of fake news on social networks. 12 Given the strength of this new phenomenon, fake news has been chosen as the word of the year by the Macquarie dictionary both in 2016 13 and in 2018 14 as well as by the Collins dictionary in 2017. 15 , 16 Since 2020, the new term “infodemic” was coined, reflecting widespread researchers’ concern (Gupta et al. 2022 ; Apuke and Omar 2021 ; Sharma et al. 2020 ; Hartley and Vu 2020 ; Micallef et al. 2020 ) about the proliferation of misinformation linked to the COVID-19 pandemic.

The Gartner Group’s top strategic predictions for 2018 and beyond included the need for IT leaders to quickly develop Artificial Intelligence (AI) algorithms to address counterfeit reality and fake news. 17 However, fake news identification is a complex issue. (Snow 2017 ) questioned the ability of AI to win the war against fake news. Similarly, other researchers concurred that even the best AI for spotting fake news is still ineffective. 18 Besides, recent studies have shown that the power of AI algorithms for identifying fake news is lower than its ability to create it Paschen ( 2019 ). Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed to closely resemble the truth in order to deceive users, and as a result, it is often hard to determine its veracity by AI alone. Therefore, it is crucial to consider more effective approaches to solve the problem of fake news in social media.

Contribution

The fake news problem has been addressed by researchers from various perspectives related to different topics. These topics include, but are not restricted to, social science studies , which investigate why and who falls for fake news (Altay et al. 2022 ; Batailler et al. 2022 ; Sterret et al. 2018 ; Badawy et al. 2019 ; Pennycook and Rand 2020 ; Weiss et al. 2020 ; Guadagno and Guttieri 2021 ), whom to trust and how perceptions of misinformation and disinformation relate to media trust and media consumption patterns (Hameleers et al. 2022 ), how fake news differs from personal lies (Chiu and Oh 2021 ; Escolà-Gascón 2021 ), examine how can the law regulate digital disinformation and how governments can regulate the values of social media companies that themselves regulate disinformation spread on their platforms (Marsden et al. 2020 ; Schuyler 2019 ; Vasu et al. 2018 ; Burshtein 2017 ; Waldman 2017 ; Alemanno 2018 ; Verstraete et al. 2017 ), and argue the challenges to democracy (Jungherr and Schroeder 2021 ); Behavioral interventions studies , which examine what literacy ideas mean in the age of dis/mis- and malinformation (Carmi et al. 2020 ), investigate whether media literacy helps identification of fake news (Jones-Jang et al. 2021 ) and attempt to improve people’s news literacy (Apuke et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers 2022 ; Nagel 2022 ; Jones-Jang et al. 2021 ; Mihailidis and Viotty 2017 ; García et al. 2020 ) by encouraging people to pause to assess credibility of headlines (Fazio 2020 ), promote civic online reasoning (McGrew 2020 ; McGrew et al. 2018 ) and critical thinking (Lutzke et al. 2019 ), together with evaluations of credibility indicators (Bhuiyan et al. 2020 ; Nygren et al. 2019 ; Shao et al. 2018a ; Pennycook et al. 2020a , b ; Clayton et al. 2020 ; Ozturk et al. 2015 ; Metzger et al. 2020 ; Sherman et al. 2020 ; Nekmat 2020 ; Brashier et al. 2021 ; Chung and Kim 2021 ; Lanius et al. 2021 ); as well as social media-driven studies , which investigate the effect of signals (e.g., sources) to detect and recognize fake news (Vraga and Bode 2017 ; Jakesch et al. 2019 ; Shen et al. 2019 ; Avram et al. 2020 ; Hameleers et al. 2020 ; Dias et al. 2020 ; Nyhan et al. 2020 ; Bode and Vraga 2015 ; Tsang 2020 ; Vishwakarma et al. 2019 ; Yavary et al. 2020 ) and investigate fake and reliable news sources using complex networks analysis based on search engine optimization metric (Mazzeo and Rapisarda 2022 ).

The impacts of fake news have reached various areas and disciplines beyond online social networks and society (García et al. 2020 ) such as economics (Clarke et al. 2020 ; Kogan et al. 2019 ; Goldstein and Yang 2019 ), psychology (Roozenbeek et al. 2020a ; Van der Linden and Roozenbeek 2020 ; Roozenbeek and van der Linden 2019 ), political science (Valenzuela et al. 2022 ; Bringula et al. 2022 ; Ricard and Medeiros 2020 ; Van der Linden et al. 2020 ; Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ), health science (Alonso-Galbán and Alemañy-Castilla 2022 ; Desai et al. 2022 ; Apuke and Omar 2021 ; Escolà-Gascón 2021 ; Wang et al. 2019c ; Hartley and Vu 2020 ; Micallef et al. 2020 ; Pennycook et al. 2020b ; Sharma et al. 2020 ; Roozenbeek et al. 2020b ), environmental science (e.g., climate change) (Treen et al. 2020 ; Lutzke et al. 2019 ; Lewandowsky 2020 ; Maertens et al. 2020 ), etc.

Interesting research has been carried out to review and study the fake news issue in online social networks. Some focus not only on fake news, but also distinguish between fake news and rumor (Bondielli and Marcelloni 2019 ; Meel and Vishwakarma 2020 ), while others tackle the whole problem, from characterization to processing techniques (Shu et al. 2017 ; Guo et al. 2020 ; Zhou and Zafarani 2020 ). However, they mostly focus on studying approaches from a machine learning perspective (Bondielli and Marcelloni 2019 ), data mining perspective (Shu et al. 2017 ), crowd intelligence perspective (Guo et al. 2020 ), or knowledge-based perspective (Zhou and Zafarani 2020 ). Furthermore, most of these studies ignore at least one of the mentioned perspectives, and in many cases, they do not cover other existing detection approaches using methods such as blockchain and fact-checking, as well as analysis on metrics used for Search Engine Optimization (Mazzeo and Rapisarda 2022 ). However, in our work and to the best of our knowledge, we cover all the approaches used for fake news detection. Indeed, we investigate the proposed solutions from broader perspectives (i.e., the detection techniques that are used, as well as the different aspects and types of the information used).

Therefore, in this paper, we are highly motivated by the following facts. First, fake news detection on social media is still in the early age of development, and many challenging issues remain that require deeper investigation. Hence, it is necessary to discuss potential research directions that can improve fake news detection and mitigation tasks. However, the dynamic nature of fake news propagation through social networks further complicates matters (Sharma et al. 2019 ). False information can easily reach and impact a large number of users in a short time (Friggeri et al. 2014 ; Qian et al. 2018 ). Moreover, fact-checking organizations cannot keep up with the dynamics of propagation as they require human verification, which can hold back a timely and cost-effective response (Kim et al. 2018 ; Ruchansky et al. 2017 ; Shu et al. 2018a ).

Our work focuses primarily on understanding the “fake news” problem, its related challenges and root causes, and reviewing automatic fake news detection and mitigation methods in online social networks as addressed by researchers. The main contributions that differentiate us from other works are summarized below:

• We present the general context from which the fake news problem emerged (i.e., online deception)
• We review existing definitions of fake news, identify the terms and features most commonly used to define fake news, and categorize related works accordingly.
• We propose a fake news typology classification based on the various categorizations of fake news reported in the literature.
• We point out the most challenging factors preventing researchers from proposing highly effective solutions for automatic fake news detection in social media.
• We highlight and classify representative studies in the domain of automatic fake news detection and mitigation on online social networks including the key methods and techniques used to generate detection models.
• We discuss the key shortcomings that may inhibit the effectiveness of the proposed fake news detection methods in online social networks.
• We provide recommendations that can help address these shortcomings and improve the quality of research in this domain.

The rest of this article is organized as follows. We explain the methodology with which the studied references are collected and selected in Sect.  2 . We introduce the online deception problem in Sect.  3 . We highlight the modern-day problem of fake news in Sect.  4 , followed by challenges facing fake news detection and mitigation tasks in Sect.  5 . We provide a comprehensive literature review of the most relevant scholarly works on fake news detection in Sect.  6 . We provide a critical discussion and recommendations that may fill some of the gaps we have identified, as well as a classification of the reviewed automatic fake news detection approaches, in Sect.  7 . Finally, we provide a conclusion and propose some future directions in Sect.  8 .

Review methodology

This section introduces the systematic review methodology on which we relied to perform our study. We start with the formulation of the research questions, which allowed us to select the relevant research literature. Then, we provide the different sources of information together with the search and inclusion/exclusion criteria we used to select the final set of papers.

Research questions formulation

The research scope, research questions, and inclusion/exclusion criteria were established following an initial evaluation of the literature and the following research questions were formulated and addressed.

• RQ1: what is fake news in social media, how is it defined in the literature, what are its related concepts, and the different types of it?
• RQ2: What are the existing challenges and issues related to fake news?
• RQ3: What are the available techniques used to perform fake news detection in social media?

Sources of information

We broadly searched for journal and conference research articles, books, and magazines as a source of data to extract relevant articles. We used the main sources of scientific databases and digital libraries in our search, such as Google Scholar, 19 IEEE Xplore, 20 Springer Link, 21 ScienceDirect, 22 Scopus, 23 ACM Digital Library. 24 Also, we screened most of the related high-profile conferences such as WWW, SIGKDD, VLDB, ICDE and so on to find out the recent work.

Search criteria

We focused our research over a period of ten years, but we made sure that about two-thirds of the research papers that we considered were published in or after 2019. Additionally, we defined a set of keywords to search the above-mentioned scientific databases since we concentrated on reviewing the current state of the art in addition to the challenges and the future direction. The set of keywords includes the following terms: fake news, disinformation, misinformation, information disorder, social media, detection techniques, detection methods, survey, literature review.

Study selection, exclusion and inclusion criteria

To retrieve relevant research articles, based on our sources of information and search criteria, a systematic keyword-based search was carried out by posing different search queries, as shown in Table  1 .

List of keywords for searching relevant articles

We discovered a primary list of articles. On the obtained initial list of studies, we applied a set of inclusion/exclusion criteria presented in Table  2 to select the appropriate research papers. The inclusion and exclusion principles are applied to determine whether a study should be included or not.

Inclusion and exclusion criteria

After reading the abstract, we excluded some articles that did not meet our criteria. We chose the most important research to help us understand the field. We reviewed the articles completely and found only 61 research papers that discuss the definition of the term fake news and its related concepts (see Table  4 ). We used the remaining papers to understand the field, reveal the challenges, review the detection techniques, and discuss future directions.

Classification of fake news definitions based on the used term and features

A brief introduction of online deception

The Cambridge Online Dictionary defines Deception as “ the act of hiding the truth, especially to get an advantage .” Deception relies on peoples’ trust, doubt and strong emotions that may prevent them from thinking and acting clearly (Aïmeur et al. 2018 ). We also define it in previous work (Aïmeur et al. 2018 ) as the process that undermines the ability to consciously make decisions and take convenient actions, following personal values and boundaries. In other words, deception gets people to do things they would not otherwise do. In the context of online deception, several factors need to be considered: the deceiver, the purpose or aim of the deception, the social media service, the deception technique and the potential target (Aïmeur et al. 2018 ; Hage et al. 2021 ).

Researchers are working on developing new ways to protect users and prevent online deception (Aïmeur et al. 2018 ). Due to the sophistication of attacks, this is a complex task. Hence, malicious attackers are using more complex tools and strategies to deceive users. Furthermore, the way information is organized and exchanged in social media may lead to exposing OSN users to many risks (Aïmeur et al. 2013 ).

In fact, this field is one of the recent research areas that need collaborative efforts of multidisciplinary practices such as psychology, sociology, journalism, computer science as well as cyber-security and digital marketing (which are not yet well explored in the field of dis/mis/malinformation but relevant for future research). Moreover, Ismailov et al. ( 2020 ) analyzed the main causes that could be responsible for the efficiency gap between laboratory results and real-world implementations.

In this paper, it is not in our scope of work to review online deception state of the art. However, we think it is crucial to note that fake news, misinformation and disinformation are indeed parts of the larger landscape of online deception (Hage et al. 2021 ).

Fake news, the modern-day problem

Fake news has existed for a very long time, much before their wide circulation became facilitated by the invention of the printing press. 25 For instance, Socrates was condemned to death more than twenty-five hundred years ago under the fake news that he was guilty of impiety against the pantheon of Athens and corruption of the youth. 26 A Google Trends Analysis of the term “fake news” reveals an explosion in popularity around the time of the 2016 US presidential election. 27 Fake news detection is a problem that has recently been addressed by numerous organizations, including the European Union 28 and NATO. 29

In this section, we first overview the fake news definitions as they were provided in the literature. We identify the terms and features used in the definitions, and we classify the latter based on them. Then, we provide a fake news typology based on distinct categorizations that we propose, and we define and compare the most cited forms of one specific fake news category (i.e., the intent-based fake news category).

Definitions of fake news

“Fake news” is defined in the Collins English Dictionary as false and often sensational information disseminated under the guise of news reporting, 30 yet the term has evolved over time and has become synonymous with the spread of false information (Cooke 2017 ).

The first definition of the term fake news was provided by Allcott and Gentzkow ( 2017 ) as news articles that are intentionally and verifiably false and could mislead readers. Then, other definitions were provided in the literature, but they all agree on the authenticity of fake news to be false (i.e., being non-factual). However, they disagree on the inclusion and exclusion of some related concepts such as satire , rumors , conspiracy theories , misinformation and hoaxes from the given definition. More recently, Nakov ( 2020 ) reported that the term fake news started to mean different things to different people, and for some politicians, it even means “news that I do not like.”

Hence, there is still no agreed definition of the term “fake news.” Moreover, we can find many terms and concepts in the literature that refer to fake news (Van der Linden et al. 2020 ; Molina et al. 2021 ) (Abu Arqoub et al. 2022 ; Allen et al. 2020 ; Allcott and Gentzkow 2017 ; Shu et al. 2017 ; Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Conroy et al. 2015 ; Celliers and Hattingh 2020 ; Nakov 2020 ; Shu et al. 2020c ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ; Egelhofer and Lecheler 2019 ; Mustafaraj and Metaxas 2017 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Lazer et al. 2018 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ), disinformation (Kapantai et al. 2021 ; Shu et al. 2020a , c ; Kumar et al. 2016 ; Bhattacharjee et al. 2020 ; Marsden et al. 2020 ; Jungherr and Schroeder 2021 ; Starbird et al. 2019 ; Ireton and Posetti 2018 ), misinformation (Wu et al. 2019 ; Shu et al. 2020c ; Shao et al. 2016 , 2018b ; Pennycook and Rand 2019 ; Micallef et al. 2020 ), malinformation (Dame Adjin-Tettey 2022 ) (Carmi et al. 2020 ; Shu et al. 2020c ), false information (Kumar and Shah 2018 ; Guo et al. 2020 ; Habib et al. 2019 ), information disorder (Shu et al. 2020c ; Wardle and Derakhshan 2017 ; Wardle 2018 ; Derakhshan and Wardle 2017 ), information warfare (Guadagno and Guttieri 2021 ) and information pollution (Meel and Vishwakarma 2020 ).

There is also a remarkable amount of disagreement over the classification of the term fake news in the research literature, as well as in policy (de Cock Buning 2018 ; ERGA 2018 , 2021 ). Some consider fake news as a type of misinformation (Allen et al. 2020 ; Singh et al. 2021 ; Ha et al. 2021 ; Pennycook and Rand 2019 ; Shao et al. 2018b ; Di Domenico et al. 2021 ; Sharma et al. 2019 ; Celliers and Hattingh 2020 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Islam et al. 2020 ), others consider it as a type of disinformation (de Cock Buning 2018 ) (Bringula et al. 2022 ; Baptista and Gradim 2022 ; Tsang 2020 ; Tandoc Jr et al. 2021 ; Bastick 2021 ; Khan et al. 2019 ; Shu et al. 2017 ; Nakov 2020 ; Shu et al. 2020c ; Egelhofer and Lecheler 2019 ), while others associate the term with both disinformation and misinformation (Wu et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers et al. 2022 ; Carmi et al. 2020 ; Allcott and Gentzkow 2017 ; Zhang and Ghorbani 2020 ; Potthast et al. 2017 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ). On the other hand, some prefer to differentiate fake news from both terms (ERGA 2018 ; Molina et al. 2021 ; ERGA 2021 ) (Zhou and Zafarani 2020 ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ).

The existing terms can be separated into two groups. The first group represents the general terms, which are information disorder , false information and fake news , each of which includes a subset of terms from the second group. The second group represents the elementary terms, which are misinformation , disinformation and malinformation . The literature agrees on the definitions of the latter group, but there is still no agreed-upon definition of the first group. In Fig.  2 , we model the relationship between the most used terms in the literature.

Modeling of the relationship between terms related to fake news

The terms most used in the literature to refer, categorize and classify fake news can be summarized and defined as shown in Table  3 , in which we capture the similarities and show the differences between the different terms based on two common key features, which are the intent and the authenticity of the news content. The intent feature refers to the intention behind the term that is used (i.e., whether or not the purpose is to mislead or cause harm), whereas the authenticity feature refers to its factual aspect. (i.e., whether the content is verifiably false or not, which we label as genuine in the second case). Some of these terms are explicitly used to refer to fake news (i.e., disinformation, misinformation and false information), while others are not (i.e., malinformation). In the comparison table, the empty dash (–) cell denotes that the classification does not apply.

A comparison between used terms based on intent and authenticity

In Fig.  3 , we identify the different features used in the literature to define fake news (i.e., intent, authenticity and knowledge). Hence, some definitions are based on two key features, which are authenticity and intent (i.e., news articles that are intentionally and verifiably false and could mislead readers). However, other definitions are based on either authenticity or intent. Other researchers categorize false information on the web and social media based on its intent and knowledge (i.e., when there is a single ground truth). In Table  4 , we classify the existing fake news definitions based on the used term and the used features . In the classification, the references in the cells refer to the research study in which a fake news definition was provided, while the empty dash (–) cells denote that the classification does not apply.

The features used for fake news definition

Fake news typology

Various categorizations of fake news have been provided in the literature. We can distinguish two major categories of fake news based on the studied perspective (i.e., intention or content) as shown in Fig.  4 . However, our proposed fake news typology is not about detection methods, and it is not exclusive. Hence, a given category of fake news can be described based on both perspectives (i.e., intention and content) at the same time. For instance, satire (i.e., intent-based fake news) can contain text and/or multimedia content types of data (e.g., headline, body, image, video) (i.e., content-based fake news) and so on.

Most researchers classify fake news based on the intent (Collins et al. 2020 ; Bondielli and Marcelloni 2019 ; Zannettou et al. 2019 ; Kumar et al. 2016 ; Wardle 2017 ; Shu et al. 2017 ; Kumar and Shah 2018 ) (see Sect.  4.2.2 ). However, other researchers (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ) focus on the content to categorize types of fake news through distinguishing the different formats and content types of data in the news (e.g., text and/or multimedia).

Recently, another classification was proposed by Zhang and Ghorbani ( 2020 ). It is based on the combination of content and intent to categorize fake news. They distinguish physical news content and non-physical news content from fake news. Physical content consists of the carriers and format of the news, and non-physical content consists of the opinions, emotions, attitudes and sentiments that the news creators want to express.

Content-based fake news category

According to researchers of this category (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ), forms of fake news may include false text such as hyperlinks or embedded content; multimedia such as false videos (Demuyakor and Opata 2022 ), images (Masciari et al. 2020 ; Shen et al. 2019 ), audios (Demuyakor and Opata 2022 ) and so on. Moreover, we can also find multimodal content (Shu et al. 2020a ) that is fake news articles and posts composed of multiple types of data combined together, for example, a fabricated image along with a text related to the image (Shu et al. 2020a ). In this category of fake news forms, we can mention as examples deepfake videos (Yang et al. 2019b ) and GAN-generated fake images (Zhang et al. 2019b ), which are artificial intelligence-based machine-generated fake content that are hard for unsophisticated social network users to identify.

The effects of these forms of fake news content vary on the credibility assessment, as well as sharing intentions which influences the spread of fake news on OSNs. For instance, people with little knowledge about the issue compared to those who are strongly concerned about the key issue of fake news tend to be easier to convince that the misleading or fake news is real, especially when shared via a video modality as compared to the text or the audio modality (Demuyakor and Opata 2022 ).

Intent-based Fake News Category

The most often mentioned and discussed forms of fake news according to researchers in this category include but are not restricted to clickbait , hoax , rumor , satire , propaganda , framing , conspiracy theories and others. In the following subsections, we explain these types of fake news as they were defined in the literature and undertake a brief comparison between them as depicted in Table  5 . The following are the most cited forms of intent-based types of fake news, and their comparison is based on what we suspect are the most common criteria mentioned by researchers.

A comparison between the different types of intent-based fake news

Clickbait refers to misleading headlines and thumbnails of content on the web (Zannettou et al. 2019 ) that tend to be fake stories with catchy headlines aimed at enticing the reader to click on a link (Collins et al. 2020 ). This type of fake news is considered to be the least severe type of false information because if a user reads/views the whole content, it is possible to distinguish if the headline and/or the thumbnail was misleading (Zannettou et al. 2019 ). However, the goal behind using clickbait is to increase the traffic to a website (Zannettou et al. 2019 ).

A hoax is a false (Zubiaga et al. 2018 ) or inaccurate (Zannettou et al. 2019 ) intentionally fabricated (Collins et al. 2020 ) news story used to masquerade the truth (Zubiaga et al. 2018 ) and is presented as factual (Zannettou et al. 2019 ) to deceive the public or audiences (Collins et al. 2020 ). This category is also known either as half-truth or factoid stories (Zannettou et al. 2019 ). Popular examples of hoaxes are stories that report the false death of celebrities (Zannettou et al. 2019 ) and public figures (Collins et al. 2020 ). Recently, hoaxes about the COVID-19 have been circulating through social media.

The term rumor refers to ambiguous or never confirmed claims (Zannettou et al. 2019 ) that are disseminated with a lack of evidence to support them (Sharma et al. 2019 ). This kind of information is widely propagated on OSNs (Zannettou et al. 2019 ). However, they are not necessarily false and may turn out to be true (Zubiaga et al. 2018 ). Rumors originate from unverified sources but may be true or false or remain unresolved (Zubiaga et al. 2018 ).

Satire refers to stories that contain a lot of irony and humor (Zannettou et al. 2019 ). It presents stories as news that might be factually incorrect, but the intent is not to deceive but rather to call out, ridicule, or to expose behavior that is shameful, corrupt, or otherwise “bad” (Golbeck et al. 2018 ). This is done with a fabricated story or by exaggerating the truth reported in mainstream media in the form of comedy (Collins et al. 2020 ). The intent behind satire seems kind of legitimate and many authors (such as Wardle (Wardle 2017 )) do include satire as a type of fake news as there is no intention to cause harm but it has the potential to mislead or fool people.

Also, Golbeck et al. ( 2018 ) mention that there is a spectrum from fake to satirical news that they found to be exploited by many fake news sites. These sites used disclaimers at the bottom of their webpages to suggest they were “satirical” even when there was nothing satirical about their articles, to protect them from accusations about being fake. The difference with a satirical form of fake news is that the authors or the host present themselves as a comedian or as an entertainer rather than a journalist informing the public (Collins et al. 2020 ). However, most audiences believed the information passed in this satirical form because the comedian usually projects news from mainstream media and frames them to suit their program (Collins et al. 2020 ).

Propaganda refers to news stories created by political entities to mislead people. It is a special instance of fabricated stories that aim to harm the interests of a particular party and, typically, has a political context (Zannettou et al. 2019 ). Propaganda was widely used during both World Wars (Collins et al. 2020 ) and during the Cold War (Zannettou et al. 2019 ). It is a consequential type of false information as it can change the course of human history (e.g., by changing the outcome of an election) (Zannettou et al. 2019 ). States are the main actors of propaganda. Recently, propaganda has been used by politicians and media organizations to support a certain position or view (Collins et al. 2020 ). Online astroturfing can be an example of the tools used for the dissemination of propaganda. It is a covert manipulation of public opinion (Peng et al. 2017 ) that aims to make it seem that many people share the same opinion about something. Astroturfing can affect different domains of interest, based on which online astroturfing can be mainly divided into political astroturfing, corporate astroturfing and astroturfing in e-commerce or online services (Mahbub et al. 2019 ). Propaganda types of fake news can be debunked with manual fact-based detection models such as the use of expert-based fact-checkers (Collins et al. 2020 ).

Framing refers to employing some aspect of reality to make content more visible, while the truth is concealed (Collins et al. 2020 ) to deceive and misguide readers. People will understand certain concepts based on the way they are coined and invented. An example of framing was provided by Collins et al. ( 2020 ): “suppose a leader X says “I will neutralize my opponent” simply meaning he will beat his opponent in a given election. Such a statement will be framed such as “leader X threatens to kill Y” and this framed statement provides a total misrepresentation of the original meaning.

Conspiracy Theories

Conspiracy theories refer to the belief that an event is the result of secret plots generated by powerful conspirators. Conspiracy belief refers to people’s adoption and belief of conspiracy theories, and it is associated with psychological, political and social factors (Douglas et al. 2019 ). Conspiracy theories are widespread in contemporary democracies (Sutton and Douglas 2020 ), and they have major consequences. For instance, lately and during the COVID-19 pandemic, conspiracy theories have been discussed from a public health perspective (Meese et al. 2020 ; Allington et al. 2020 ; Freeman et al. 2020 ).

Comparison Between Most Popular Intent-based Types of Fake News

Following a review of the most popular intent-based types of fake news, we compare them as shown in Table  5 based on the most common criteria mentioned by researchers in their definitions as listed below.

• the intent behind the news, which refers to whether a given news type was mainly created to intentionally deceive people or not (e.g., humor, irony, entertainment, etc.);
• the way that the news propagates through OSN, which determines the nature of the propagation of each type of fake news and this can be either fast or slow propagation;
• the severity of the impact of the news on OSN users, which refers to whether the public has been highly impacted by the given type of fake news; the mentioned impact of each fake news type is mainly the proportion of the negative impact;
• and the goal behind disseminating the news, which can be to gain popularity for a particular entity (e.g., political party), for profit (e.g., lucrative business), or other reasons such as humor and irony in the case of satire, spreading panic or anger, and manipulating the public in the case of hoaxes, made-up stories about a particular person or entity in the case of rumors, and misguiding readers in the case of framing.

However, the comparison provided in Table  5 is deduced from the studied research papers; it is our point of view, which is not based on empirical data.

We suspect that the most dangerous types of fake news are the ones with high intention to deceive the public, fast propagation through social media, high negative impact on OSN users, and complicated hidden goals and agendas. However, while the other types of fake news are less dangerous, they should not be ignored.

Moreover, it is important to highlight that the existence of the overlap in the types of fake news mentioned above has been proven, thus it is possible to observe false information that may fall within multiple categories (Zannettou et al. 2019 ). Here, we provide two examples by Zannettou et al. ( 2019 ) to better understand possible overlaps: (1) a rumor may also use clickbait techniques to increase the audience that will read the story; and (2) propaganda stories, as a special instance of a framing story.

Challenges related to fake news detection and mitigation

To alleviate fake news and its threats, it is crucial to first identify and understand the factors involved that continue to challenge researchers. Thus, the main question is to explore and investigate the factors that make it easier to fall for manipulated information. Despite the tremendous progress made in alleviating some of the challenges in fake news detection (Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Shu et al. 2020a ), much more work needs to be accomplished to address the problem effectively.

In this section, we discuss several open issues that have been making fake news detection in social media a challenging problem. These issues can be summarized as follows: content-based issues (i.e., deceptive content that resembles the truth very closely), contextual issues (i.e., lack of user awareness, social bots spreaders of fake content, and OSN’s dynamic natures that leads to the fast propagation), as well as the issue of existing datasets (i.e., there still no one size fits all benchmark dataset for fake news detection). These various aspects have proven (Shu et al. 2017 ) to have a great impact on the accuracy of fake news detection approaches.

Content-based issue, deceptive content

Automatic fake news detection remains a huge challenge, primarily because the content is designed in a way that it closely resembles the truth. Besides, most deceivers choose their words carefully and use their language strategically to avoid being caught. Therefore, it is often hard to determine its veracity by AI without the reliance on additional information from third parties such as fact-checkers.

Abdullah-All-Tanvir et al. ( 2020 ) reported that fake news tends to have more complicated stories and hardly ever make any references. It is more likely to contain a greater number of words that express negative emotions. This makes it so complicated that it becomes impossible for a human to manually detect the credibility of this content. Therefore, detecting fake news on social media is quite challenging. Moreover, fake news appears in multiple types and forms, which makes it hard and challenging to define a single global solution able to capture and deal with the disseminated content. Consequently, detecting false information is not a straightforward task due to its various types and forms Zannettou et al. ( 2019 ).

Contextual issues

Contextual issues are challenges that we suspect may not be related to the content of the news but rather they are inferred from the context of the online news post (i.e., humans are the weakest factor due to lack of user awareness, social bots spreaders, dynamic nature of online social platforms and fast propagation of fake news).

Humans are the weakest factor due to the lack of awareness

Recent statistics 31 show that the percentage of unintentional fake news spreaders (people who share fake news without the intention to mislead) over social media is five times higher than intentional spreaders. Moreover, another recent statistic 32 shows that the percentage of people who were confident about their ability to discern fact from fiction is ten times higher than those who were not confident about the truthfulness of what they are sharing. As a result, we can deduce the lack of human awareness about the ascent of fake news.

Public susceptibility and lack of user awareness (Sharma et al. 2019 ) have always been the most challenging problem when dealing with fake news and misinformation. This is a complex issue because many people believe almost everything on the Internet and the ones who are new to digital technology or have less expertise may be easily fooled (Edgerly et al. 2020 ).

Moreover, it has been widely proven (Metzger et al. 2020 ; Edgerly et al. 2020 ) that people are often motivated to support and accept information that goes with their preexisting viewpoints and beliefs, and reject information that does not fit in as well. Hence, Shu et al. ( 2017 ) illustrate an interesting correlation between fake news spread and psychological and cognitive theories. They further suggest that humans are more likely to believe information that confirms their existing views and ideological beliefs. Consequently, they deduce that humans are naturally not very good at differentiating real information from fake information.

Recent research by Giachanou et al. ( 2020 ) studies the role of personality and linguistic patterns in discriminating between fake news spreaders and fact-checkers. They classify a user as a potential fact-checker or a potential fake news spreader based on features that represent users’ personality traits and linguistic patterns used in their tweets. They show that leveraging personality traits and linguistic patterns can improve the performance in differentiating between checkers and spreaders.

Furthermore, several researchers studied the prevalence of fake news on social networks during (Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ) and after (Garrett and Bond 2021 ) the 2016 US presidential election and found that individuals most likely to engage with fake news sources were generally conservative-leaning, older, and highly engaged with political news.

Metzger et al. ( 2020 ) examine how individuals evaluate the credibility of biased news sources and stories. They investigate the role of both cognitive dissonance and credibility perceptions in selective exposure to attitude-consistent news information. They found that online news consumers tend to perceive attitude-consistent news stories as more accurate and more credible than attitude-inconsistent stories.

Similarly, Edgerly et al. ( 2020 ) explore the impact of news headlines on the audience’s intent to verify whether given news is true or false. They concluded that participants exhibit higher intent to verify the news only when they believe the headline to be true, which is predicted by perceived congruence with preexisting ideological tendencies.

Luo et al. ( 2022 ) evaluate the effects of endorsement cues in social media on message credibility and detection accuracy. Results showed that headlines associated with a high number of likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news. Consequently, they highlight the urgency of empowering individuals to assess both news veracity and endorsement cues appropriately on social media.

Moreover, misinformed people are a greater problem than uninformed people (Kuklinski et al. 2000 ), because the former hold inaccurate opinions (which may concern politics, climate change, medicine) that are harder to correct. Indeed, people find it difficult to update their misinformation-based beliefs even after they have been proved to be false (Flynn et al. 2017 ). Moreover, even if a person has accepted the corrected information, his/her belief may still affect their opinion (Nyhan and Reifler 2015 ).

Falling for disinformation may also be explained by a lack of critical thinking and of the need for evidence that supports information (Vilmer et al. 2018 ; Badawy et al. 2019 ). However, it is also possible that people choose misinformation because they engage in directionally motivated reasoning (Badawy et al. 2019 ; Flynn et al. 2017 ). Online clients are normally vulnerable and will, in general, perceive web-based networking media as reliable, as reported by Abdullah-All-Tanvir et al. ( 2019 ), who propose to mechanize fake news recognition.

It is worth noting that in addition to bots causing the outpouring of the majority of the misrepresentations, specific individuals are also contributing a large share of this issue (Abdullah-All-Tanvir et al. 2019 ). Furthermore, Vosoughi et al. (Vosoughi et al. 2018 ) found that contrary to conventional wisdom, robots have accelerated the spread of real and fake news at the same rate, implying that fake news spreads more than the truth because humans, not robots, are more likely to spread it.

In this case, verified users and those with numerous followers were not necessarily responsible for spreading misinformation of the corrupted posts (Abdullah-All-Tanvir et al. 2019 ).

Viral fake news can cause much havoc to our society. Therefore, to mitigate the negative impact of fake news, it is important to analyze the factors that lead people to fall for misinformation and to further understand why people spread fake news (Cheng et al. 2020 ). Measuring the accuracy, credibility, veracity and validity of news contents can also be a key countermeasure to consider.

Social bots spreaders

Several authors (Shu et al. 2018b , 2017 ; Shi et al. 2019 ; Bessi and Ferrara 2016 ; Shao et al. 2018a ) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016 ). Bots (short for software robots) exist since the early days of computers. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior (Ferrara et al. 2016 ). Although they are designed to provide a useful service, they can be harmful, for example when they contribute to the spread of unverified information or rumors (Ferrara et al. 2016 ). However, it is important to note that bots are simply tools created and maintained by humans for some specific hidden agendas.

Social bots tend to connect with legitimate users instead of other bots. They try to act like a human with fewer words and fewer followers on social media. This contributes to the forwarding of fake news (Jiang et al. 2019 ). Moreover, there is a difference between bot-generated and human-written clickbait (Le et al. 2019 ).

Many researchers have addressed ways of identifying and analyzing possible sources of fake news spread in social media. Recent research by Shu et al. ( 2020a ) describes social bots use of two strategies to spread low-credibility content. First, they amplify interactions with content as soon as it is created to make it look legitimate and to facilitate its spread across social networks. Next, they try to increase public exposure to the created content and thus boost its perceived credibility by targeting influential users that are more likely to believe disinformation in the hope of getting them to “repost” the fabricated content. They further discuss the social bot detection systems taxonomy proposed by Ferrara et al. ( 2016 ) which divides bot detection methods into three classes: (1) graph-based, (2) crowdsourcing and (3) feature-based social bot detection methods.

Similarly, Shao et al. ( 2018a ) examine social bots and how they promote the spread of misinformation through millions of Twitter posts during and following the 2016 US presidential campaign. They found that social bots played a disproportionate role in spreading articles from low-credibility sources by amplifying such content in the early spreading moments and targeting users with many followers through replies and mentions to expose them to this content and induce them to share it.

Ismailov et al. ( 2020 ) assert that the techniques used to detect bots depend on the social platform and the objective. They note that a malicious bot designed to make friends with as many accounts as possible will require a different detection approach than a bot designed to repeatedly post links to malicious websites. Therefore, they identify two models for detecting malicious accounts, each using a different set of features. Social context models achieve detection by examining features related to an account’s social presence including features such as relationships to other accounts, similarities to other users’ behaviors, and a variety of graph-based features. User behavior models primarily focus on features related to an individual user’s behavior, such as frequency of activities (e.g., number of tweets or posts per time interval), patterns of activity and clickstream sequences.

Therefore, it is crucial to consider bot detection techniques to distinguish bots from normal users to better leverage user profile features to detect fake news.

However, there is also another “bot-like” strategy that aims to massively promote disinformation and fake content in social platforms, which is called bot farms or also troll farms. It is not social bots, but it is a group of organized individuals engaging in trolling or bot-like promotion of narratives in a coordinated fashion (Wardle 2018 ) hired to massively spread fake news or any other harmful content. A prominent troll farm example is the Russia-based Internet Research Agency (IRA), which disseminated inflammatory content online to influence the outcome of the 2016 U.S. presidential election. 33 As a result, Twitter suspended accounts connected to the IRA and deleted 200,000 tweets from Russian trolls (Jamieson 2020 ). Another example to mention in this category is review bombing (Moro and Birt 2022 ). Review bombing refers to coordinated groups of people massively performing the same negative actions online (e.g., dislike, negative review/comment) on an online video, game, post, product, etc., in order to reduce its aggregate review score. The review bombers can be both humans and bots coordinated in order to cause harm and mislead people by falsifying facts.

Dynamic nature of online social platforms and fast propagation of fake news

Sharma et al. ( 2019 ) affirm that the fast proliferation of fake news through social networks makes it hard and challenging to assess the information’s credibility on social media. Similarly, Qian et al. ( 2018 ) assert that fake news and fabricated content propagate exponentially at the early stage of its creation and can cause a significant loss in a short amount of time (Friggeri et al. 2014 ) including manipulating the outcome of political events (Liu and Wu 2018 ; Bessi and Ferrara 2016 ).

Moreover, while analyzing the way source and promoters of fake news operate over the web through multiple online platforms, Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to real information (11%).

Furthermore, recently, Shu et al. ( 2020c ) attempted to understand the propagation of disinformation and fake news in social media and found that such content is produced and disseminated faster and easier through social media because of the low barriers that prevent doing so. Similarly, Shu et al. ( 2020b ) studied hierarchical propagation networks for fake news detection. They performed a comparative analysis between fake and real news from structural, temporal and linguistic perspectives. They demonstrated the potential of using these features to detect fake news and they showed their effectiveness for fake news detection as well.

Lastly, Abdullah-All-Tanvir et al. ( 2020 ) note that it is almost impossible to manually detect the sources and authenticity of fake news effectively and efficiently, due to its fast circulation in such a small amount of time. Therefore, it is crucial to note that the dynamic nature of the various online social platforms, which results in the continued rapid and exponential propagation of such fake content, remains a major challenge that requires further investigation while defining innovative solutions for fake news detection.

Datasets issue

The existing approaches lack an inclusive dataset with derived multidimensional information to detect fake news characteristics to achieve higher accuracy of machine learning classification model performance (Nyow and Chua 2019 ). These datasets are primarily dedicated to validating the machine learning model and are the ultimate frame of reference to train the model and analyze its performance. Therefore, if a researcher evaluates their model based on an unrepresentative dataset, the validity and the efficiency of the model become questionable when it comes to applying the fake news detection approach in a real-world scenario.

Moreover, several researchers (Shu et al. 2020d ; Wang et al. 2020 ; Pathak and Srihari 2019 ; Przybyla 2020 ) believe that fake news is diverse and dynamic in terms of content, topics, publishing methods and media platforms, and sophisticated linguistic styles geared to emulate true news. Consequently, training machine learning models on such sophisticated content requires large-scale annotated fake news data that are difficult to obtain (Shu et al. 2020d ).

Therefore, datasets are also a great topic to work on to enhance data quality and have better results while defining our solutions. Adversarial learning techniques (e.g., GAN, SeqGAN) can be used to provide machine-generated data that can be used to train deeper models and build robust systems to detect fake examples from the real ones. This approach can be used to counter the lack of datasets and the scarcity of data available to train models.

Fake news detection literature review

Fake news detection in social networks is still in the early stage of development and there are still challenging issues that need further investigation. This has become an emerging research area that is attracting huge attention.

There are various research studies on fake news detection in online social networks. Few of them have focused on the automatic detection of fake news using artificial intelligence techniques. In this section, we review the existing approaches used in automatic fake news detection, as well as the techniques that have been adopted. Then, a critical discussion built on a primary classification scheme based on a specific set of criteria is also emphasized.

Categories of fake news detection

In this section, we give an overview of most of the existing automatic fake news detection solutions adopted in the literature. A recent classification by Sharma et al. ( 2019 ) uses three categories of fake news identification methods. Each category is further divided based on the type of existing methods (i.e., content-based, feedback-based and intervention-based methods). However, a review of the literature for fake news detection in online social networks shows that the existing studies can be classified into broader categories based on two major aspects that most authors inspect and make use of to define an adequate solution. These aspects can be considered as major sources of extracted information used for fake news detection and can be summarized as follows: the content-based (i.e., related to the content of the news post) and the contextual aspect (i.e., related to the context of the news post).

Consequently, the studies we reviewed can be classified into three different categories based on the two aspects mentioned above (the third category is hybrid). As depicted in Fig.  5 , fake news detection solutions can be categorized as news content-based approaches, the social context-based approaches that can be divided into network and user-based approaches, and hybrid approaches. The latter combines both content-based and contextual approaches to define the solution.

Classification of fake news detection approaches

News Content-based Category

News content-based approaches are fake news detection approaches that use content information (i.e., information extracted from the content of the news post) and that focus on studying and exploiting the news content in their proposed solutions. Content refers to the body of the news, including source, headline, text and image-video, which can reflect subtle differences.

Researchers of this category rely on content-based detection cues (i.e., text and multimedia-based cues), which are features extracted from the content of the news post. Text-based cues are features extracted from the text of the news, whereas multimedia-based cues are features extracted from the images and videos attached to the news. Figure  6 summarizes the most widely used news content representation (i.e., text and multimedia/images) and detection techniques (i.e., machine learning (ML), deep Learning (DL), natural language processing (NLP), fact-checking, crowdsourcing (CDS) and blockchain (BKC)) in news content-based category of fake news detection approaches. Most of the reviewed research works based on news content for fake news detection rely on the text-based cues (Kapusta et al. 2019 ; Kaur et al. 2020 ; Vereshchaka et al. 2020 ; Ozbay and Alatas 2020 ; Wang 2017 ; Nyow and Chua 2019 ; Hosseinimotlagh and Papalexakis 2018 ; Abdullah-All-Tanvir et al. 2019 , 2020 ; Mahabub 2020 ; Bahad et al. 2019 ; Hiriyannaiah et al. 2020 ) extracted from the text of the news content including the body of the news and its headline. However, a few researchers such as Vishwakarma et al. ( 2019 ) and Amri et al. ( 2022 ) try to recognize text from the associated image.

News content-based category: news content representation and detection techniques

Most researchers of this category rely on artificial intelligence (AI) techniques (such as ML, DL and NLP models) to improve performance in terms of prediction accuracy. Others use different techniques such as fact-checking, crowdsourcing and blockchain. Specifically, the AI- and ML-based approaches in this category are trying to extract features from the news content, which they use later for content analysis and training tasks. In this particular case, the extracted features are the different types of information considered to be relevant for the analysis. Feature extraction is considered as one of the best techniques to reduce data size in automatic fake news detection. This technique aims to choose a subset of features from the original set to improve classification performance (Yazdi et al. 2020 ).

Table  6 lists the distinct features and metadata, as well as the used datasets in the news content-based category of fake news detection approaches.

The features and datasets used in the news content-based approaches

a https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

b https://mediabiasfactcheck.com/ , last access date: 26-12-2022

c https://github.com/KaiDMML/FakeNewsNet , last access date: 26-12-2022

d https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

e https://www.cs.ucsb.edu/~william/data/liar_dataset.zip , last access date: 26-12-2022

f https://www.kaggle.com/mrisdal/fake-news , last access date: 26-12-2022

g https://github.com/BuzzFeedNews/2016-10-facebook-fact-check , last access date: 26-12-2022

h https://www.politifact.com/subjects/fake-news/ , last access date: 26-12-2022

i https://www.kaggle.com/rchitic17/real-or-fake , last access date: 26-12-2022

j https://www.kaggle.com/jruvika/fake-news-detection , last access date: 26-12-2022

k https://github.com/MKLab-ITI/image-verification-corpus , last access date: 26-12-2022

l https://drive.google.com/file/d/14VQ7EWPiFeGzxp3XC2DeEHi-BEisDINn/view , last access date: 26-12-2022

Social Context-based Category

Unlike news content-based solutions, the social context-based approaches capture the skeptical social context of the online news (Zhang and Ghorbani 2020 ) rather than focusing on the news content. The social context-based category contains fake news detection approaches that use the contextual aspects (i.e., information related to the context of the news post). These aspects are based on social context and they offer additional information to help detect fake news. They are the surrounding data outside of the fake news article itself, where they can be an essential part of automatic fake news detection. Some useful examples of contextual information may include checking if the news itself and the source that published it are credible, checking the date of the news or the supporting resources, and checking if any other online news platforms are reporting the same or similar stories (Zhang and Ghorbani 2020 ).

Social context-based aspects can be classified into two subcategories, user-based and network-based, and they can be used for context analysis and training tasks in the case of AI- and ML-based approaches. User-based aspects refer to information captured from OSN users such as user profile information (Shu et al. 2019b ; Wang et al. 2019c ; Hamdi et al. 2020 ; Nyow and Chua 2019 ; Jiang et al. 2019 ) and user behavior (Cardaioli et al. 2020 ) such as user engagement (Uppada et al. 2022 ; Jiang et al. 2019 ; Shu et al. 2018b ; Nyow and Chua 2019 ) and response (Zhang et al. 2019a ; Qian et al. 2018 ). Meanwhile, network-based aspects refer to information captured from the properties of the social network where the fake content is shared and disseminated such as news propagation path (Liu and Wu 2018 ; Wu and Liu 2018 ) (e.g., propagation times and temporal characteristics of propagation), diffusion patterns (Shu et al. 2019a ) (e.g., number of retweets, shares), as well as user relationships (Mishra 2020 ; Hamdi et al. 2020 ; Jiang et al. 2019 ) (e.g., friendship status among users).

Figure  7 summarizes some of the most widely adopted social context representations, as well as the most used detection techniques (i.e., AI, ML, DL, fact-checking and blockchain), in the social context-based category of approaches.

Social context-based category: social context representation and detection techniques

Table  7 lists the distinct features and metadata, the adopted detection cues, as well as the used datasets, in the context-based category of fake news detection approaches.

The features, detection cues and datasets used int the social context-based approaches

a https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip , last access date: 26-12-2022 b https://snap.stanford.edu/data/ego-Twitter.html , last access date: 26-12-2022

Hybrid approaches

Most researchers are focusing on employing a specific method rather than a combination of both content- and context-based methods. This is because some of them (Wu and Rao 2020 ) believe that there still some challenging limitations in the traditional fusion strategies due to existing feature correlations and semantic conflicts. For this reason, some researchers focus on extracting content-based information, while others are capturing some social context-based information for their proposed approaches.

However, it has proven challenging to successfully automate fake news detection based on just a single type of feature (Ruchansky et al. 2017 ). Therefore, recent directions tend to do a mixture by using both news content-based and social context-based approaches for fake news detection.

Table  8 lists the distinct features and metadata, as well as the used datasets, in the hybrid category of fake news detection approaches.

The features and datasets used in the hybrid approaches

Fake news detection techniques

Another vision for classifying automatic fake news detection is to look at techniques used in the literature. Hence, we classify the detection methods based on the techniques into three groups:

• Human-based techniques: This category mainly includes the use of crowdsourcing and fact-checking techniques, which rely on human knowledge to check and validate the veracity of news content.
• Artificial Intelligence-based techniques: This category includes the most used AI approaches for fake news detection in the literature. Specifically, these are the approaches in which researchers use classical ML, deep learning techniques such as convolutional neural network (CNN), recurrent neural network (RNN), as well as natural language processing (NLP).
• Blockchain-based techniques: This category includes solutions using blockchain technology to detect and mitigate fake news in social media by checking source reliability and establishing the traceability of the news content.

Human-based Techniques

One specific research direction for fake news detection consists of using human-based techniques such as crowdsourcing (Pennycook and Rand 2019 ; Micallef et al. 2020 ) and fact-checking (Vlachos and Riedel 2014 ; Chung and Kim 2021 ; Nyhan et al. 2020 ) techniques.

These approaches can be considered as low computational requirement techniques since both rely on human knowledge and expertise for fake news detection. However, fake news identification cannot be addressed solely through human force since it demands a lot of effort in terms of time and cost, and it is ineffective in terms of preventing the fast spread of fake content.

Crowdsourcing. Crowdsourcing approaches (Kim et al. 2018 ) are based on the “wisdom of the crowds” (Collins et al. 2020 ) for fake content detection. These approaches rely on the collective contributions and crowd signals (Tschiatschek et al. 2018 ) of a group of people for the aggregation of crowd intelligence to detect fake news (Tchakounté et al. 2020 ) and to reduce the spread of misinformation on social media (Pennycook and Rand 2019 ; Micallef et al. 2020 ).

Micallef et al. ( 2020 ) highlight the role of the crowd in countering misinformation. They suspect that concerned citizens (i.e., the crowd), who use platforms where disinformation appears, can play a crucial role in spreading fact-checking information and in combating the spread of misinformation.

Recently Tchakounté et al. ( 2020 ) proposed a voting system as a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party site.

Similarly, Huffaker et al. ( 2020 ) propose a crowdsourced detection of emotionally manipulative language. They introduce an approach that transforms classification problems into a comparison task to mitigate conflation content by allowing the crowd to detect text that uses manipulative emotional language to sway users toward positions or actions. The proposed system leverages anchor comparison to distinguish between intrinsically emotional content and emotionally manipulative language.

La Barbera et al. ( 2020 ) try to understand how people perceive the truthfulness of information presented to them. They collect data from US-based crowd workers, build a dataset of crowdsourced truthfulness judgments for political statements, and compare it with expert annotation data generated by fact-checkers such as PolitiFact.

Coscia and Rossi ( 2020 ) introduce a crowdsourced flagging system that consists of online news flagging. The bipolar model of news flagging attempts to capture the main ingredients that they observe in empirical research on fake news and disinformation.

Unlike the previously mentioned researchers who focus on news content in their approaches, Pennycook and Rand ( 2019 ) focus on using crowdsourced judgments of the quality of news sources to combat social media disinformation.

Fact-Checking. The fact-checking task is commonly manually performed by journalists to verify the truthfulness of a given claim. Indeed, fact-checking features are being adopted by multiple online social network platforms. For instance, Facebook 34 started addressing false information through independent fact-checkers in 2017, followed by Google 35 the same year. Two years later, Instagram 36 followed suit. However, the usefulness of fact-checking initiatives is questioned by journalists 37 , as well as by researchers such as Andersen and Søe ( 2020 ). On the other hand, work is being conducted to boost the effectiveness of these initiatives to reduce misinformation (Chung and Kim 2021 ; Clayton et al. 2020 ; Nyhan et al. 2020 ).

Most researchers use fact-checking websites (e.g., politifact.com, 38 snopes.com, 39 Reuters, 40 , etc.) as data sources to build their datasets and train their models. Therefore, in the following, we specifically review examples of solutions that use fact-checking (Vlachos and Riedel 2014 ) to help build datasets that can be further used in the automatic detection of fake content.

Yang et al. ( 2019a ) use PolitiFact fact-checking website as a data source to train, tune, and evaluate their model named XFake, on political data. The XFake system is an explainable fake news detector that assists end users to identify news credibility. The fakeness of news items is detected and interpreted considering both content and contextual (e.g., statements) information (e.g., speaker).

Based on the idea that fact-checkers cannot clean all data, and it must be a selection of what “matters the most” to clean while checking a claim, Sintos et al. ( 2019 ) propose a solution to help fact-checkers combat problems related to data quality (where inaccurate data lead to incorrect conclusions) and data phishing. The proposed solution is a combination of data cleaning and perturbation analysis to avoid uncertainties and errors in data and the possibility that data can be phished.

Tchechmedjiev et al. ( 2019 ) propose a system named “ClaimsKG” as a knowledge graph of fact-checked claims aiming to facilitate structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. “ClaimsKG” designs the relationship between vocabularies. To gather vocabularies, a semi-automated pipeline periodically gathers data from popular fact-checking websites regularly.

AI-based Techniques

Previous work by Yaqub et al. ( 2020 ) has shown that people lack trust in automated solutions for fake news detection However, work is already being undertaken to increase this trust, for instance by von der Weth et al. ( 2020 ).

Most researchers consider fake news detection as a classification problem and use artificial intelligence techniques, as shown in Fig.  8 . The adopted AI techniques may include machine learning ML (e.g., Naïve Bayes, logistic regression, support vector machine SVM), deep learning DL (e.g., convolutional neural networks CNN, recurrent neural networks RNN, long short-term memory LSTM) and natural language processing NLP (e.g., Count vectorizer, TF-IDF Vectorizer). Most of them combine many AI techniques in their solutions rather than relying on one specific approach.

Examples of the most widely used AI techniques for fake news detection

Many researchers are developing machine learning models in their solutions for fake news detection. Recently, deep neural network techniques are also being employed as they are generating promising results (Islam et al. 2020 ). A neural network is a massively parallel distributed processor with simple units that can store important information and make it available for use (Hiriyannaiah et al. 2020 ). Moreover, it has been proven (Cardoso Durier da Silva et al. 2019 ) that the most widely used method for automatic detection of fake news is not simply a classical machine learning technique, but rather a fusion of classical techniques coordinated by a neural network.

Some researchers define purely machine learning models (Del Vicario et al. 2019 ; Elhadad et al. 2019 ; Aswani et al. 2017 ; Hakak et al. 2021 ; Singh et al. 2021 ) in their fake news detection approaches. The more commonly used machine learning algorithms (Abdullah-All-Tanvir et al. 2019 ) for classification problems are Naïve Bayes, logistic regression and SVM.

Other researchers (Wang et al. 2019c ; Wang 2017 ; Liu and Wu 2018 ; Mishra 2020 ; Qian et al. 2018 ; Zhang et al. 2020 ; Goldani et al. 2021 ) prefer to do a mixture of different deep learning models, without combining them with classical machine learning techniques. Some even prove that deep learning techniques outperform traditional machine learning techniques (Mishra et al. 2022 ). Deep learning is one of the most widely popular research topics in machine learning. Unlike traditional machine learning approaches, which are based on manually crafted features, deep learning approaches can learn hidden representations from simpler inputs both in context and content variations (Bondielli and Marcelloni 2019 ). Moreover, traditional machine learning algorithms almost always require structured data and are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets, which requires human intervention to “teach them” when the result is incorrect (Parrish 2018 ), while deep learning networks rely on layers of artificial neural networks (ANN) and do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes (Parrish 2018 ). The two most widely implemented paradigms in deep neural networks are recurrent neural networks (RNN) and convolutional neural networks (CNN).

Still other researchers (Abdullah-All-Tanvir et al. 2019 ; Kaliyar et al. 2020 ; Zhang et al. 2019a ; Deepak and Chitturi 2020 ; Shu et al. 2018a ; Wang et al. 2019c ) prefer to combine traditional machine learning and deep learning classification, models. Others combine machine learning and natural language processing techniques. A few combine deep learning models with natural language processing (Vereshchaka et al. 2020 ). Some other researchers (Kapusta et al. 2019 ; Ozbay and Alatas 2020 ; Ahmed et al. 2020 ) combine natural language processing with machine learning models. Furthermore, others (Abdullah-All-Tanvir et al. 2019 ; Kaur et al. 2020 ; Kaliyar 2018 ; Abdullah-All-Tanvir et al. 2020 ; Bahad et al. 2019 ) prefer to combine all the previously mentioned techniques (i.e., ML, DL and NLP) in their approaches.

Table  11 , which is relegated to the Appendix (after the bibliography) because of its size, shows a comparison of the fake news detection solutions that we have reviewed based on their main approaches, the methodology that was used and the models.

Comparison of AI-based fake news detection techniques

Blockchain-based Techniques for Source Reliability and Traceability

Another research direction for detecting and mitigating fake news in social media focuses on using blockchain solutions. Blockchain technology is recently attracting researchers’ attention due to the interesting features it offers. Immutability, decentralization, tamperproof, consensus, record keeping and non-repudiation of transactions are some of the key features that make blockchain technology exploitable, not just for cryptocurrencies, but also to prove the authenticity and integrity of digital assets.

However, the proposed blockchain approaches are few in number and they are fundamental and theoretical approaches. Specifically, the solutions that are currently available are still in research, prototype, and beta testing stages (DiCicco and Agarwal 2020 ; Tchechmedjiev et al. 2019 ). Furthermore, most researchers (Ochoa et al. 2019 ; Song et al. 2019 ; Shang et al. 2018 ; Qayyum et al. 2019 ; Jing and Murugesan 2018 ; Buccafurri et al. 2017 ; Chen et al. 2018 ) do not specify which fake news type they are mitigating in their studies. They mention news content in general, which is not adequate for innovative solutions. For that, serious implementations should be provided to prove the usefulness and feasibility of this newly developing research vision.

Table  9 shows a classification of the reviewed blockchain-based approaches. In the classification, we listed the following:

• The type of fake news that authors are trying to mitigate, which can be multimedia-based or text-based fake news.
• The techniques used for fake news mitigation, which can be either blockchain only, or blockchain combined with other techniques such as AI, Data mining, Truth-discovery, Preservation metadata, Semantic similarity, Crowdsourcing, Graph theory and SIR model (Susceptible, Infected, Recovered).
• The feature that is offered as an advantage of the given solution (e.g., Reliability, Authenticity and Traceability). Reliability is the credibility and truthfulness of the news content, which consists of proving the trustworthiness of the content. Traceability aims to trace and archive the contents. Authenticity consists of checking whether the content is real and authentic.

A checkmark ( ✓ ) in Table  9 denotes that the mentioned criterion is explicitly mentioned in the proposed solution, while the empty dash (–) cell for fake news type denotes that it depends on the case: The criterion was either not explicitly mentioned (e.g., fake news type) in the work or the classification does not apply (e.g., techniques/other).

A classification of popular blockchain-based approaches for fake news detection in social media

After reviewing the most relevant state of the art for automatic fake news detection, we classify them as shown in Table  10 based on the detection aspects (i.e., content-based, contextual, or hybrid aspects) and the techniques used (i.e., AI, crowdsourcing, fact-checking, blockchain or hybrid techniques). Hybrid techniques refer to solutions that simultaneously combine different techniques from previously mentioned categories (i.e., inter-hybrid methods), as well as techniques within the same class of methods (i.e., intra-hybrid methods), in order to define innovative solutions for fake news detection. A hybrid method should bring the best of both worlds. Then, we provide a discussion based on different axes.

Fake news detection approaches classification

News content-based methods

Most of the news content-based approaches consider fake news detection as a classification problem and they use AI techniques such as classical machine learning (e.g., regression, Bayesian) as well as deep learning (i.e., neural methods such as CNN and RNN). More specifically, classification of social media content is a fundamental task for social media mining, so that most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags (Wu and Liu 2018 ). The main challenge facing these approaches is how to extract features in a way to reduce the data used to train their models and what features are the most suitable for accurate results.

Researchers using such approaches are motivated by the fact that the news content is the main entity in the deception process, and it is a straightforward factor to analyze and use while looking for predictive clues of deception. However, detecting fake news only from the content of the news is not enough because the news is created in a strategic intentional way to mimic the truth (i.e., the content can be intentionally manipulated by the spreader to make it look like real news). Therefore, it is considered to be challenging, if not impossible, to identify useful features (Wu and Liu 2018 ) and consequently tell the nature of such news solely from the content.

Moreover, works that utilize only the news content for fake news detection ignore the rich information and latent user intelligence (Qian et al. 2018 ) stored in user responses toward previously disseminated articles. Therefore, the auxiliary information is deemed crucial for an effective fake news detection approach.

Social context-based methods

The context-based approaches explore the surrounding data outside of the news content, which can be an effective direction and has some advantages in areas where the content approaches based on text classification can run into issues. However, most existing studies implementing contextual methods mainly focus on additional information coming from users and network diffusion patterns. Moreover, from a technical perspective, they are limited to the use of sophisticated machine learning techniques for feature extraction, and they ignore the usefulness of results coming from techniques such as web search and crowdsourcing which may save much time and help in the early detection and identification of fake content.

Hybrid approaches can simultaneously model different aspects of fake news such as the content-based aspects, as well as the contextual aspect based on both the OSN user and the OSN network patterns. However, these approaches are deemed more complex in terms of models (Bondielli and Marcelloni 2019 ), data availability, and the number of features. Furthermore, it remains difficult to decide which information among each category (i.e., content-based and context-based information) is most suitable and appropriate to be used to achieve accurate and precise results. Therefore, there are still very few studies belonging to this category of hybrid approaches.

Early detection

As fake news usually evolves and spreads very fast on social media, it is critical and urgent to consider early detection directions. Yet, this is a challenging task to do especially in highly dynamic platforms such as social networks. Both news content- and social context-based approaches suffer from this challenging early detection of fake news.

Although approaches that detect fake news based on content analysis face this issue less, they are still limited by the lack of information required for verification when the news is in its early stage of spread. However, approaches that detect fake news based on contextual analysis are most likely to suffer from the lack of early detection since most of them rely on information that is mostly available after the spread of fake content such as social engagement, user response, and propagation patterns. Therefore, it is crucial to consider both trusted human verification and historical data as an attempt to detect fake content during its early stage of propagation.

Conclusion and future directions

In this paper, we introduced the general context of the fake news problem as one of the major issues of the online deception problem in online social networks. Based on reviewing the most relevant state of the art, we summarized and classified existing definitions of fake news, as well as its related terms. We also listed various typologies and existing categorizations of fake news such as intent-based fake news including clickbait, hoax, rumor, satire, propaganda, conspiracy theories, framing as well as content-based fake news including text and multimedia-based fake news, and in the latter, we can tackle deepfake videos and GAN-generated fake images. We discussed the major challenges related to fake news detection and mitigation in social media including the deceptiveness nature of the fabricated content, the lack of human awareness in the field of fake news, the non-human spreaders issue (e.g., social bots), the dynamicity of such online platforms, which results in a fast propagation of fake content and the quality of existing datasets, which still limits the efficiency of the proposed solutions. We reviewed existing researchers’ visions regarding the automatic detection of fake news based on the adopted approaches (i.e., news content-based approaches, social context-based approaches, or hybrid approaches) and the techniques that are used (i.e., artificial intelligence-based methods; crowdsourcing, fact-checking, and blockchain-based methods; and hybrid methods), then we showed a comparative study between the reviewed works. We also provided a critical discussion of the reviewed approaches based on different axes such as the adopted aspect for fake news detection (i.e., content-based, contextual, and hybrid aspects) and the early detection perspective.

To conclude, we present the main issues for combating the fake news problem that needs to be further investigated while proposing new detection approaches. We believe that to define an efficient fake news detection approach, we need to consider the following:

• Our choice of sources of information and search criteria may have introduced biases in our research. If so, it would be desirable to identify those biases and mitigate them.
• News content is the fundamental source to find clues to distinguish fake from real content. However, contextual information derived from social media users and from the network can provide useful auxiliary information to increase detection accuracy. Specifically, capturing users’ characteristics and users’ behavior toward shared content can be a key task for fake news detection.
• Moreover, capturing users’ historical behavior, including their emotions and/or opinions toward news content, can help in the early detection and mitigation of fake news.
• Furthermore, adversarial learning techniques (e.g., GAN, SeqGAN) can be considered as a promising direction for mitigating the lack and scarcity of available datasets by providing machine-generated data that can be used to train and build robust systems to detect the fake examples from the real ones.
• Lastly, analyzing how sources and promoters of fake news operate over the web through multiple online platforms is crucial; Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to valid information (11%).

Appendix: A Comparison of AI-based fake news detection techniques

This Appendix consists only in the rather long Table  11 . It shows a comparison of the fake news detection solutions based on artificial intelligence that we have reviewed according to their main approaches, the methodology that was used, and the models, as explained in Sect.  6.2.2 .

Author Contributions

The order of authors is alphabetic as is customary in the third author’s field. The lead author was Sabrine Amri, who collected and analyzed the data and wrote a first draft of the paper, all along under the supervision and tight guidance of Esma Aïmeur. Gilles Brassard reviewed, criticized and polished the work into its final form.

This work is supported in part by Canada’s Natural Sciences and Engineering Research Council.

Availability of data and material

Declarations.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

1 https://www.nationalacademies.org/news/2021/07/as-surgeon-general-urges-whole-of-society-effort-to-fight-health-misinformation-the-work-of-the-national-academies-helps-foster-an-evidence-based-information-environment , last access date: 26-12-2022.

2 https://time.com/4897819/elvis-presley-alive-conspiracy-theories/ , last access date: 26-12-2022.

3 https://www.therichest.com/shocking/the-evidence-15-reasons-people-think-the-earth-is-flat/ , last access date: 26-12-2022.

4 https://www.grunge.com/657584/the-truth-about-1952s-alien-invasion-of-washington-dc/ , last access date: 26-12-2022.

5 https://www.journalism.org/2021/01/12/news-use-across-social-media-platforms-in-2020/ , last access date: 26-12-2022.

6 https://www.pewresearch.org/fact-tank/2018/12/10/social-media-outpaces-print-newspapers-in-the-u-s-as-a-news-source/ , last access date: 26-12-2022.

7 https://www.buzzfeednews.com/article/janelytvynenko/coronavirus-fake-news-disinformation-rumors-hoaxes , last access date: 26-12-2022.

8 https://www.factcheck.org/2020/03/viral-social-media-posts-offer-false-coronavirus-tips/ , last access date: 26-12-2022.

9 https://www.factcheck.org/2020/02/fake-coronavirus-cures-part-2-garlic-isnt-a-cure/ , last access date: 26-12-2022.

10 https://www.bbc.com/news/uk-36528256 , last access date: 26-12-2022.

11 https://en.wikipedia.org/wiki/Pizzagate_conspiracy_theory , last access date: 26-12-2022.

12 https://www.theguardian.com/world/2017/jan/09/germany-investigating-spread-fake-news-online-russia-election , last access date: 26-12-2022.

13 https://www.macquariedictionary.com.au/resources/view/word/of/the/year/2016 , last access date: 26-12-2022.

14 https://www.macquariedictionary.com.au/resources/view/word/of/the/year/2018 , last access date: 26-12-2022.

15 https://apnews.com/article/47466c5e260149b1a23641b9e319fda6 , last access date: 26-12-2022.

16 https://blog.collinsdictionary.com/language-lovers/collins-2017-word-of-the-year-shortlist/ , last access date: 26-12-2022.

17 https://www.gartner.com/smarterwithgartner/gartner-top-strategic-predictions-for-2018-and-beyond/ , last access date: 26-12-2022.

18 https://www.technologyreview.com/s/612236/even-the-best-ai-for-spotting-fake-news-is-still-terrible/ , last access date: 26-12-2022.

19 https://scholar.google.ca/ , last access date: 26-12-2022.

20 https://ieeexplore.ieee.org/ , last access date: 26-12-2022.

21 https://link.springer.com/ , last access date: 26-12-2022.

22 https://www.sciencedirect.com/ , last access date: 26-12-2022.

23 https://www.scopus.com/ , last access date: 26-12-2022.

24 https://www.acm.org/digital-library , last access date: 26-12-2022.

25 https://www.politico.com/magazine/story/2016/12/fake-news-history-long-violent-214535 , last access date: 26-12-2022.

26 https://en.wikipedia.org/wiki/Trial_of_Socrates , last access date: 26-12-2022.

27 https://trends.google.com/trends/explore?hl=en-US &tz=-180 &date=2013-12-06+2018-01-06 &geo=US &q=fake+news &sni=3 , last access date: 26-12-2022.

28 https://ec.europa.eu/digital-single-market/en/tackling-online-disinformation , last access date: 26-12-2022.

29 https://www.nato.int/cps/en/natohq/177273.htm , last access date: 26-12-2022.

30 https://www.collinsdictionary.com/dictionary/english/fake-news , last access date: 26-12-2022.

31 https://www.statista.com/statistics/657111/fake-news-sharing-online/ , last access date: 26-12-2022.

32 https://www.statista.com/statistics/657090/fake-news-recogition-confidence/ , last access date: 26-12-2022.

33 https://www.nbcnews.com/tech/social-media/now-available-more-200-000-deleted-russian-troll-tweets-n844731 , last access date: 26-12-2022.

34 https://www.theguardian.com/technology/2017/mar/22/facebook-fact-checking-tool-fake-news , last access date: 26-12-2022.

35 https://www.theguardian.com/technology/2017/apr/07/google-to-display-fact-checking-labels-to-show-if-news-is-true-or-false , last access date: 26-12-2022.

36 https://about.instagram.com/blog/announcements/combatting-misinformation-on-instagram , last access date: 26-12-2022.

37 https://www.wired.com/story/instagram-fact-checks-who-will-do-checking/ , last access date: 26-12-2022.

38 https://www.politifact.com/ , last access date: 26-12-2022.

39 https://www.snopes.com/ , last access date: 26-12-2022.

40 https://www.reutersagency.com/en/ , last access date: 26-12-2022.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Esma Aïmeur, Email: ac.laertnomu.ori@ruemia .

Sabrine Amri, Email: [email protected] .

Gilles Brassard, Email: ac.laertnomu.ori@drassarb .

• Abdullah-All-Tanvir, Mahir EM, Akhter S, Huq MR (2019) Detecting fake news using machine learning and deep learning algorithms. In: 7th international conference on smart computing and communications (ICSCC), IEEE, pp 1–5 10.1109/ICSCC.2019.8843612
• Abdullah-All-Tanvir, Mahir EM, Huda SMA, Barua S (2020) A hybrid approach for identifying authentic news using deep learning methods on popular Twitter threads. In: International conference on artificial intelligence and signal processing (AISP), IEEE, pp 1–6 10.1109/AISP48273.2020.9073583
• Abu Arqoub O, Abdulateef Elega A, Efe Özad B, Dwikat H, Adedamola Oloyede F. Mapping the scholarship of fake news research: a systematic review. J Pract. 2022; 16 (1):56–86. doi: 10.1080/17512786.2020.1805791. [ CrossRef ] [ Google Scholar ]
• Ahmed S, Hinkelmann K, Corradini F. Development of fake news model using machine learning through natural language processing. Int J Comput Inf Eng. 2020; 14 (12):454–460. [ Google Scholar ]
• Aïmeur E, Brassard G, Rioux J. Data privacy: an end-user perspective. Int J Comput Netw Commun Secur. 2013; 1 (6):237–250. [ Google Scholar ]
• Aïmeur E, Hage H, Amri S (2018) The scourge of online deception in social networks. In: 2018 international conference on computational science and computational intelligence (CSCI), IEEE, pp 1266–1271 10.1109/CSCI46756.2018.00244
• Alemanno A. How to counter fake news? A taxonomy of anti-fake news approaches. Eur J Risk Regul. 2018; 9 (1):1–5. doi: 10.1017/err.2018.12. [ CrossRef ] [ Google Scholar ]
• Allcott H, Gentzkow M. Social media and fake news in the 2016 election. J Econ Perspect. 2017; 31 (2):211–36. doi: 10.1257/jep.31.2.211. [ CrossRef ] [ Google Scholar ]
• Allen J, Howland B, Mobius M, Rothschild D, Watts DJ. Evaluating the fake news problem at the scale of the information ecosystem. Sci Adv. 2020 doi: 10.1126/sciadv.aay3539. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Allington D, Duffy B, Wessely S, Dhavan N, Rubin J. Health-protective behaviour, social media usage and conspiracy belief during the Covid-19 public health emergency. Psychol Med. 2020 doi: 10.1017/S003329172000224X. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Alonso-Galbán P, Alemañy-Castilla C (2022) Curbing misinformation and disinformation in the Covid-19 era: a view from cuba. MEDICC Rev 22:45–46 10.37757/MR2020.V22.N2.12 [ PubMed ] [ CrossRef ]
• Altay S, Hacquin AS, Mercier H. Why do so few people share fake news? It hurts their reputation. New Media Soc. 2022; 24 (6):1303–1324. doi: 10.1177/1461444820969893. [ CrossRef ] [ Google Scholar ]
• Amri S, Sallami D, Aïmeur E (2022) Exmulf: an explainable multimodal content-based fake news detection system. In: International symposium on foundations and practice of security. Springer, Berlin, pp 177–187. 10.1109/IJCNN48605.2020.9206973
• Andersen J, Søe SO. Communicative actions we live by: the problem with fact-checking, tagging or flagging fake news-the case of Facebook. Eur J Commun. 2020; 35 (2):126–139. doi: 10.1177/0267323119894489. [ CrossRef ] [ Google Scholar ]
• Apuke OD, Omar B. Fake news and Covid-19: modelling the predictors of fake news sharing among social media users. Telematics Inform. 2021; 56 :101475. doi: 10.1016/j.tele.2020.101475. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Apuke OD, Omar B, Tunca EA, Gever CV. The effect of visual multimedia instructions against fake news spread: a quasi-experimental study with Nigerian students. J Librariansh Inf Sci. 2022 doi: 10.1177/09610006221096477. [ CrossRef ] [ Google Scholar ]
• Aswani R, Ghrera S, Kar AK, Chandra S. Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc Netw Anal Min. 2017; 7 (1):1–10. doi: 10.1007/s13278-017-0461-2. [ CrossRef ] [ Google Scholar ]
• Avram M, Micallef N, Patil S, Menczer F (2020) Exposure to social engagement metrics increases vulnerability to misinformation. arXiv preprint arxiv:2005.04682 , 10.37016/mr-2020-033
• Badawy A, Lerman K, Ferrara E (2019) Who falls for online political manipulation? In: Companion proceedings of the 2019 world wide web conference, pp 162–168 10.1145/3308560.3316494
• Bahad P, Saxena P, Kamal R. Fake news detection using bi-directional LSTM-recurrent neural network. Procedia Comput Sci. 2019; 165 :74–82. doi: 10.1016/j.procs.2020.01.072. [ CrossRef ] [ Google Scholar ]
• Bakdash J, Sample C, Rankin M, Kantarcioglu M, Holmes J, Kase S, Zaroukian E, Szymanski B (2018) The future of deception: machine-generated and manipulated images, video, and audio? In: 2018 international workshop on social sensing (SocialSens), IEEE, pp 2–2 10.1109/SocialSens.2018.00009
• Balmas M. When fake news becomes real: combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Commun Res. 2014; 41 (3):430–454. doi: 10.1177/0093650212453600. [ CrossRef ] [ Google Scholar ]
• Baptista JP, Gradim A. Understanding fake news consumption: a review. Soc Sci. 2020 doi: 10.3390/socsci9100185. [ CrossRef ] [ Google Scholar ]
• Baptista JP, Gradim A. A working definition of fake news. Encyclopedia. 2022; 2 (1):632–645. doi: 10.3390/encyclopedia2010043. [ CrossRef ] [ Google Scholar ]
• Bastick Z. Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation. Comput Hum Behav. 2021; 116 :106633. doi: 10.1016/j.chb.2020.106633. [ CrossRef ] [ Google Scholar ]
• Batailler C, Brannon SM, Teas PE, Gawronski B. A signal detection approach to understanding the identification of fake news. Perspect Psychol Sci. 2022; 17 (1):78–98. doi: 10.1177/1745691620986135. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bessi A, Ferrara E (2016) Social bots distort the 2016 US presidential election online discussion. First Monday 21(11-7). 10.5210/fm.v21i11.7090
• Bhattacharjee A, Shu K, Gao M, Liu H (2020) Disinformation in the online information ecosystem: detection, mitigation and challenges. arXiv preprint arXiv:2010.09113
• Bhuiyan MM, Zhang AX, Sehat CM, Mitra T. Investigating differences in crowdsourced news credibility assessment: raters, tasks, and expert criteria. Proc ACM Hum Comput Interact. 2020; 4 (CSCW2):1–26. doi: 10.1145/3415164. [ CrossRef ] [ Google Scholar ]
• Bode L, Vraga EK. In related news, that was wrong: the correction of misinformation through related stories functionality in social media. J Commun. 2015; 65 (4):619–638. doi: 10.1111/jcom.12166. [ CrossRef ] [ Google Scholar ]
• Bondielli A, Marcelloni F. A survey on fake news and rumour detection techniques. Inf Sci. 2019; 497 :38–55. doi: 10.1016/j.ins.2019.05.035. [ CrossRef ] [ Google Scholar ]
• Bovet A, Makse HA. Influence of fake news in Twitter during the 2016 US presidential election. Nat Commun. 2019; 10 (1):1–14. doi: 10.1038/s41467-018-07761-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brashier NM, Pennycook G, Berinsky AJ, Rand DG. Timing matters when correcting fake news. Proc Natl Acad Sci. 2021 doi: 10.1073/pnas.2020043118. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brewer PR, Young DG, Morreale M. The impact of real news about “fake news”: intertextual processes and political satire. Int J Public Opin Res. 2013; 25 (3):323–343. doi: 10.1093/ijpor/edt015. [ CrossRef ] [ Google Scholar ]
• Bringula RP, Catacutan-Bangit AE, Garcia MB, Gonzales JPS, Valderama AMC. “Who is gullible to political disinformation?” Predicting susceptibility of university students to fake news. J Inf Technol Polit. 2022; 19 (2):165–179. doi: 10.1080/19331681.2021.1945988. [ CrossRef ] [ Google Scholar ]
• Buccafurri F, Lax G, Nicolazzo S, Nocera A (2017) Tweetchain: an alternative to blockchain for crowd-based applications. In: International conference on web engineering, Springer, Berlin, pp 386–393. 10.1007/978-3-319-60131-1_24
• Burshtein S. The true story on fake news. Intell Prop J. 2017; 29 (3):397–446. [ Google Scholar ]
• Cardaioli M, Cecconello S, Conti M, Pajola L, Turrin F (2020) Fake news spreaders profiling through behavioural analysis. In: CLEF (working notes)
• Cardoso Durier da Silva F, Vieira R, Garcia AC (2019) Can machines learn to detect fake news? A survey focused on social media. In: Proceedings of the 52nd Hawaii international conference on system sciences. 10.24251/HICSS.2019.332
• Carmi E, Yates SJ, Lockley E, Pawluczuk A (2020) Data citizenship: rethinking data literacy in the age of disinformation, misinformation, and malinformation. Intern Policy Rev 9(2):1–22 10.14763/2020.2.1481
• Celliers M, Hattingh M (2020) A systematic review on fake news themes reported in literature. In: Conference on e-Business, e-Services and e-Society. Springer, Berlin, pp 223–234. 10.1007/978-3-030-45002-1_19
• Chen Y, Li Q, Wang H (2018) Towards trusted social networks with blockchain technology. arXiv preprint arXiv:1801.02796
• Cheng L, Guo R, Shu K, Liu H (2020) Towards causal understanding of fake news dissemination. arXiv preprint arXiv:2010.10580
• Chiu MM, Oh YW. How fake news differs from personal lies. Am Behav Sci. 2021; 65 (2):243–258. doi: 10.1177/0002764220910243. [ CrossRef ] [ Google Scholar ]
• Chung M, Kim N. When I learn the news is false: how fact-checking information stems the spread of fake news via third-person perception. Hum Commun Res. 2021; 47 (1):1–24. doi: 10.1093/hcr/hqaa010. [ CrossRef ] [ Google Scholar ]
• Clarke J, Chen H, Du D, Hu YJ. Fake news, investor attention, and market reaction. Inf Syst Res. 2020 doi: 10.1287/isre.2019.0910. [ CrossRef ] [ Google Scholar ]
• Clayton K, Blair S, Busam JA, Forstner S, Glance J, Green G, Kawata A, Kovvuri A, Martin J, Morgan E, et al. Real solutions for fake news? Measuring the effectiveness of general warnings and fact-check tags in reducing belief in false stories on social media. Polit Behav. 2020; 42 (4):1073–1095. doi: 10.1007/s11109-019-09533-0. [ CrossRef ] [ Google Scholar ]
• Collins B, Hoang DT, Nguyen NT, Hwang D (2020) Fake news types and detection models on social media a state-of-the-art survey. In: Asian conference on intelligent information and database systems. Springer, Berlin, pp 562–573 10.1007/978-981-15-3380-8_49
• Conroy NK, Rubin VL, Chen Y. Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol. 2015; 52 (1):1–4. doi: 10.1002/pra2.2015.145052010082. [ CrossRef ] [ Google Scholar ]
• Cooke NA. Posttruth, truthiness, and alternative facts: Information behavior and critical information consumption for a new age. Libr Q. 2017; 87 (3):211–221. doi: 10.1086/692298. [ CrossRef ] [ Google Scholar ]
• Coscia M, Rossi L. Distortions of political bias in crowdsourced misinformation flagging. J R Soc Interface. 2020; 17 (167):20200020. doi: 10.1098/rsif.2020.0020. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dame Adjin-Tettey T. Combating fake news, disinformation, and misinformation: experimental evidence for media literacy education. Cogent Arts Human. 2022; 9 (1):2037229. doi: 10.1080/23311983.2022.2037229. [ CrossRef ] [ Google Scholar ]
• Deepak S, Chitturi B. Deep neural approach to fake-news identification. Procedia Comput Sci. 2020; 167 :2236–2243. doi: 10.1016/j.procs.2020.03.276. [ CrossRef ] [ Google Scholar ]
• de Cock Buning M (2018) A multi-dimensional approach to disinformation: report of the independent high level group on fake news and online disinformation. Publications Office of the European Union
• Del Vicario M, Quattrociocchi W, Scala A, Zollo F. Polarization and fake news: early warning of potential misinformation targets. ACM Trans Web (TWEB) 2019; 13 (2):1–22. doi: 10.1145/3316809. [ CrossRef ] [ Google Scholar ]
• Demuyakor J, Opata EM. Fake news on social media: predicting which media format influences fake news most on facebook. J Intell Commun. 2022 doi: 10.54963/jic.v2i1.56. [ CrossRef ] [ Google Scholar ]
• Derakhshan H, Wardle C (2017) Information disorder: definitions. In: Understanding and addressing the disinformation ecosystem, pp 5–12
• Desai AN, Ruidera D, Steinbrink JM, Granwehr B, Lee DH. Misinformation and disinformation: the potential disadvantages of social media in infectious disease and how to combat them. Clin Infect Dis. 2022; 74 (Supplement–3):e34–e39. doi: 10.1093/cid/ciac109. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Di Domenico G, Sit J, Ishizaka A, Nunan D. Fake news, social media and marketing: a systematic review. J Bus Res. 2021; 124 :329–341. doi: 10.1016/j.jbusres.2020.11.037. [ CrossRef ] [ Google Scholar ]
• Dias N, Pennycook G, Rand DG. Emphasizing publishers does not effectively reduce susceptibility to misinformation on social media. Harv Kennedy School Misinform Rev. 2020 doi: 10.37016/mr-2020-001. [ CrossRef ] [ Google Scholar ]
• DiCicco KW, Agarwal N (2020) Blockchain technology-based solutions to fight misinformation: a survey. In: Disinformation, misinformation, and fake news in social media. Springer, Berlin, pp 267–281, 10.1007/978-3-030-42699-6_14
• Douglas KM, Uscinski JE, Sutton RM, Cichocka A, Nefes T, Ang CS, Deravi F. Understanding conspiracy theories. Polit Psychol. 2019; 40 :3–35. doi: 10.1111/pops.12568. [ CrossRef ] [ Google Scholar ]
• Edgerly S, Mourão RR, Thorson E, Tham SM. When do audiences verify? How perceptions about message and source influence audience verification of news headlines. J Mass Commun Q. 2020; 97 (1):52–71. doi: 10.1177/1077699019864680. [ CrossRef ] [ Google Scholar ]
• Egelhofer JL, Lecheler S. Fake news as a two-dimensional phenomenon: a framework and research agenda. Ann Int Commun Assoc. 2019; 43 (2):97–116. doi: 10.1080/23808985.2019.1602782. [ CrossRef ] [ Google Scholar ]
• Elhadad MK, Li KF, Gebali F (2019) A novel approach for selecting hybrid features from online news textual metadata for fake news detection. In: International conference on p2p, parallel, grid, cloud and internet computing. Springer, Berlin, pp 914–925, 10.1007/978-3-030-33509-0_86
• ERGA (2018) Fake news, and the information disorder. European Broadcasting Union (EBU)
• ERGA (2021) Notions of disinformation and related concepts. European Regulators Group for Audiovisual Media Services (ERGA)
• Escolà-Gascón Á. New techniques to measure lie detection using Covid-19 fake news and the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2) Comput Hum Behav Rep. 2021; 3 :100049. doi: 10.1016/j.chbr.2020.100049. [ CrossRef ] [ Google Scholar ]
• Fazio L. Pausing to consider why a headline is true or false can help reduce the sharing of false news. Harv Kennedy School Misinformation Rev. 2020 doi: 10.37016/mr-2020-009. [ CrossRef ] [ Google Scholar ]
• Ferrara E, Varol O, Davis C, Menczer F, Flammini A. The rise of social bots. Commun ACM. 2016; 59 (7):96–104. doi: 10.1145/2818717. [ CrossRef ] [ Google Scholar ]
• Flynn D, Nyhan B, Reifler J. The nature and origins of misperceptions: understanding false and unsupported beliefs about politics. Polit Psychol. 2017; 38 :127–150. doi: 10.1111/pops.12394. [ CrossRef ] [ Google Scholar ]
• Fraga-Lamas P, Fernández-Caramés TM. Fake news, disinformation, and deepfakes: leveraging distributed ledger technologies and blockchain to combat digital deception and counterfeit reality. IT Prof. 2020; 22 (2):53–59. doi: 10.1109/MITP.2020.2977589. [ CrossRef ] [ Google Scholar ]
• Freeman D, Waite F, Rosebrock L, Petit A, Causier C, East A, Jenner L, Teale AL, Carr L, Mulhall S, et al. Coronavirus conspiracy beliefs, mistrust, and compliance with government guidelines in England. Psychol Med. 2020 doi: 10.1017/S0033291720001890. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Friggeri A, Adamic L, Eckles D, Cheng J (2014) Rumor cascades. In: Proceedings of the international AAAI conference on web and social media
• García SA, García GG, Prieto MS, Moreno Guerrero AJ, Rodríguez Jiménez C. The impact of term fake news on the scientific community. Scientific performance and mapping in web of science. Soc Sci. 2020 doi: 10.3390/socsci9050073. [ CrossRef ] [ Google Scholar ]
• Garrett RK, Bond RM. Conservatives’ susceptibility to political misperceptions. Sci Adv. 2021 doi: 10.1126/sciadv.abf1234. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Giachanou A, Ríssola EA, Ghanem B, Crestani F, Rosso P (2020) The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In: International conference on applications of natural language to information systems. Springer, Berlin, pp 181–192 10.1007/978-3-030-51310-8_17
• Golbeck J, Mauriello M, Auxier B, Bhanushali KH, Bonk C, Bouzaghrane MA, Buntain C, Chanduka R, Cheakalos P, Everett JB et al (2018) Fake news vs satire: a dataset and analysis. In: Proceedings of the 10th ACM conference on web science, pp 17–21, 10.1145/3201064.3201100
• Goldani MH, Momtazi S, Safabakhsh R. Detecting fake news with capsule neural networks. Appl Soft Comput. 2021; 101 :106991. doi: 10.1016/j.asoc.2020.106991. [ CrossRef ] [ Google Scholar ]
• Goldstein I, Yang L. Good disclosure, bad disclosure. J Financ Econ. 2019; 131 (1):118–138. doi: 10.1016/j.jfineco.2018.08.004. [ CrossRef ] [ Google Scholar ]
• Grinberg N, Joseph K, Friedland L, Swire-Thompson B, Lazer D. Fake news on Twitter during the 2016 US presidential election. Science. 2019; 363 (6425):374–378. doi: 10.1126/science.aau2706. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guadagno RE, Guttieri K (2021) Fake news and information warfare: an examination of the political and psychological processes from the digital sphere to the real world. In: Research anthology on fake news, political warfare, and combatting the spread of misinformation. IGI Global, pp 218–242 10.4018/978-1-7998-7291-7.ch013
• Guess A, Nagler J, Tucker J. Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci Adv. 2019 doi: 10.1126/sciadv.aau4586. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guo C, Cao J, Zhang X, Shu K, Yu M (2019) Exploiting emotions for fake news detection on social media. arXiv preprint arXiv:1903.01728
• Guo B, Ding Y, Yao L, Liang Y, Yu Z. The future of false information detection on social media: new perspectives and trends. ACM Comput Surv (CSUR) 2020; 53 (4):1–36. doi: 10.1145/3393880. [ CrossRef ] [ Google Scholar ]
• Gupta A, Li H, Farnoush A, Jiang W. Understanding patterns of covid infodemic: a systematic and pragmatic approach to curb fake news. J Bus Res. 2022; 140 :670–683. doi: 10.1016/j.jbusres.2021.11.032. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ha L, Andreu Perez L, Ray R. Mapping recent development in scholarship on fake news and misinformation, 2008 to 2017: disciplinary contribution, topics, and impact. Am Behav Sci. 2021; 65 (2):290–315. doi: 10.1177/0002764219869402. [ CrossRef ] [ Google Scholar ]
• Habib A, Asghar MZ, Khan A, Habib A, Khan A. False information detection in online content and its role in decision making: a systematic literature review. Soc Netw Anal Min. 2019; 9 (1):1–20. doi: 10.1007/s13278-019-0595-5. [ CrossRef ] [ Google Scholar ]
• Hage H, Aïmeur E, Guedidi A (2021) Understanding the landscape of online deception. In: Research anthology on fake news, political warfare, and combatting the spread of misinformation. IGI Global, pp 39–66. 10.4018/978-1-7998-2543-2.ch014
• Hakak S, Alazab M, Khan S, Gadekallu TR, Maddikunta PKR, Khan WZ. An ensemble machine learning approach through effective feature extraction to classify fake news. Futur Gener Comput Syst. 2021; 117 :47–58. doi: 10.1016/j.future.2020.11.022. [ CrossRef ] [ Google Scholar ]
• Hamdi T, Slimi H, Bounhas I, Slimani Y (2020) A hybrid approach for fake news detection in Twitter based on user features and graph embedding. In: International conference on distributed computing and internet technology. Springer, Berlin, pp 266–280. 10.1007/978-3-030-36987-3_17
• Hameleers M. Separating truth from lies: comparing the effects of news media literacy interventions and fact-checkers in response to political misinformation in the us and netherlands. Inf Commun Soc. 2022; 25 (1):110–126. doi: 10.1080/1369118X.2020.1764603. [ CrossRef ] [ Google Scholar ]
• Hameleers M, Powell TE, Van Der Meer TG, Bos L. A picture paints a thousand lies? The effects and mechanisms of multimodal disinformation and rebuttals disseminated via social media. Polit Commun. 2020; 37 (2):281–301. doi: 10.1080/10584609.2019.1674979. [ CrossRef ] [ Google Scholar ]
• Hameleers M, Brosius A, de Vreese CH. Whom to trust? media exposure patterns of citizens with perceptions of misinformation and disinformation related to the news media. Eur J Commun. 2022 doi: 10.1177/02673231211072667. [ CrossRef ] [ Google Scholar ]
• Hartley K, Vu MK. Fighting fake news in the Covid-19 era: policy insights from an equilibrium model. Policy Sci. 2020; 53 (4):735–758. doi: 10.1007/s11077-020-09405-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hasan HR, Salah K. Combating deepfake videos using blockchain and smart contracts. IEEE Access. 2019; 7 :41596–41606. doi: 10.1109/ACCESS.2019.2905689. [ CrossRef ] [ Google Scholar ]
• Hiriyannaiah S, Srinivas A, Shetty GK, Siddesh G, Srinivasa K (2020) A computationally intelligent agent for detecting fake news using generative adversarial networks. Hybrid computational intelligence: challenges and applications. pp 69–96 10.1016/B978-0-12-818699-2.00004-4
• Hosseinimotlagh S, Papalexakis EE (2018) Unsupervised content-based identification of fake news articles with tensor decomposition ensembles. In: Proceedings of the workshop on misinformation and misbehavior mining on the web (MIS2)
• Huckle S, White M. Fake news: a technological approach to proving the origins of content, using blockchains. Big Data. 2017; 5 (4):356–371. doi: 10.1089/big.2017.0071. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huffaker JS, Kummerfeld JK, Lasecki WS, Ackerman MS (2020) Crowdsourced detection of emotionally manipulative language. In: Proceedings of the 2020 CHI conference on human factors in computing systems. pp 1–14 10.1145/3313831.3376375
• Ireton C, Posetti J. Journalism, fake news & disinformation: handbook for journalism education and training. Paris: UNESCO Publishing; 2018. [ Google Scholar ]
• Islam MR, Liu S, Wang X, Xu G. Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc Netw Anal Min. 2020; 10 (1):1–20. doi: 10.1007/s13278-020-00696-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ismailov M, Tsikerdekis M, Zeadally S. Vulnerabilities to online social network identity deception detection research and recommendations for mitigation. Fut Internet. 2020; 12 (9):148. doi: 10.3390/fi12090148. [ CrossRef ] [ Google Scholar ]
• Jakesch M, Koren M, Evtushenko A, Naaman M (2019) The role of source and expressive responding in political news evaluation. In: Computation and journalism symposium
• Jamieson KH. Cyberwar: how Russian hackers and trolls helped elect a president: what we don’t, can’t, and do know. Oxford: Oxford University Press; 2020. [ Google Scholar ]
• Jiang S, Chen X, Zhang L, Chen S, Liu H (2019) User-characteristic enhanced model for fake news detection in social media. In: CCF International conference on natural language processing and Chinese computing, Springer, Berlin, pp 634–646. 10.1007/978-3-030-32233-5_49
• Jin Z, Cao J, Zhang Y, Luo J (2016) News verification by exploiting conflicting social viewpoints in microblogs. In: Proceedings of the AAAI conference on artificial intelligence
• Jing TW, Murugesan RK (2018) A theoretical framework to build trust and prevent fake news in social media using blockchain. In: International conference of reliable information and communication technology. Springer, Berlin, pp 955–962, 10.1007/978-3-319-99007-1_88
• Jones-Jang SM, Mortensen T, Liu J. Does media literacy help identification of fake news? Information literacy helps, but other literacies don’t. Am Behav Sci. 2021; 65 (2):371–388. doi: 10.1177/0002764219869406. [ CrossRef ] [ Google Scholar ]
• Jungherr A, Schroeder R. Disinformation and the structural transformations of the public arena: addressing the actual challenges to democracy. Soc Media Soc. 2021 doi: 10.1177/2056305121988928. [ CrossRef ] [ Google Scholar ]
• Kaliyar RK (2018) Fake news detection using a deep neural network. In: 2018 4th international conference on computing communication and automation (ICCCA), IEEE, pp 1–7 10.1109/CCAA.2018.8777343
• Kaliyar RK, Goswami A, Narang P, Sinha S. Fndnet—a deep convolutional neural network for fake news detection. Cogn Syst Res. 2020; 61 :32–44. doi: 10.1016/j.cogsys.2019.12.005. [ CrossRef ] [ Google Scholar ]
• Kapantai E, Christopoulou A, Berberidis C, Peristeras V. A systematic literature review on disinformation: toward a unified taxonomical framework. New Media Soc. 2021; 23 (5):1301–1326. doi: 10.1177/1461444820959296. [ CrossRef ] [ Google Scholar ]
• Kapusta J, Benko L, Munk M (2019) Fake news identification based on sentiment and frequency analysis. In: International conference Europe middle east and North Africa information systems and technologies to support learning. Springer, Berlin, pp 400–409, 10.1007/978-3-030-36778-7_44
• Kaur S, Kumar P, Kumaraguru P. Automating fake news detection system using multi-level voting model. Soft Comput. 2020; 24 (12):9049–9069. doi: 10.1007/s00500-019-04436-y. [ CrossRef ] [ Google Scholar ]
• Khan SA, Alkawaz MH, Zangana HM (2019) The use and abuse of social media for spreading fake news. In: 2019 IEEE international conference on automatic control and intelligent systems (I2CACIS), IEEE, pp 145–148. 10.1109/I2CACIS.2019.8825029
• Kim J, Tabibian B, Oh A, Schölkopf B, Gomez-Rodriguez M (2018) Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 324–332. 10.1145/3159652.3159734
• Klein D, Wueller J. Fake news: a legal perspective. J Internet Law. 2017; 20 (10):5–13. [ Google Scholar ]
• Kogan S, Moskowitz TJ, Niessner M (2019) Fake news: evidence from financial markets. Available at SSRN 3237763
• Kuklinski JH, Quirk PJ, Jerit J, Schwieder D, Rich RF. Misinformation and the currency of democratic citizenship. J Polit. 2000; 62 (3):790–816. doi: 10.1111/0022-3816.00033. [ CrossRef ] [ Google Scholar ]
• Kumar S, Shah N (2018) False information on web and social media: a survey. arXiv preprint arXiv:1804.08559
• Kumar S, West R, Leskovec J (2016) Disinformation on the web: impact, characteristics, and detection of Wikipedia hoaxes. In: Proceedings of the 25th international conference on world wide web, pp 591–602. 10.1145/2872427.2883085
• La Barbera D, Roitero K, Demartini G, Mizzaro S, Spina D (2020) Crowdsourcing truthfulness: the impact of judgment scale and assessor bias. In: European conference on information retrieval. Springer, Berlin, pp 207–214. 10.1007/978-3-030-45442-5_26
• Lanius C, Weber R, MacKenzie WI. Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey. Soc Netw Anal Min. 2021; 11 (1):1–15. doi: 10.1007/s13278-021-00739-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D, et al. The science of fake news. Science. 2018; 359 (6380):1094–1096. doi: 10.1126/science.aao2998. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Le T, Shu K, Molina MD, Lee D, Sundar SS, Liu H (2019) 5 sources of clickbaits you should know! Using synthetic clickbaits to improve prediction and distinguish between bot-generated and human-written headlines. In: 2019 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 33–40. 10.1145/3341161.3342875
• Lewandowsky S (2020) Climate change, disinformation, and how to combat it. In: Annual Review of Public Health 42. 10.1146/annurev-publhealth-090419-102409 [ PubMed ]
• Liu Y, Wu YF (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence, pp 354–361
• Luo M, Hancock JT, Markowitz DM. Credibility perceptions and detection accuracy of fake news headlines on social media: effects of truth-bias and endorsement cues. Commun Res. 2022; 49 (2):171–195. doi: 10.1177/0093650220921321. [ CrossRef ] [ Google Scholar ]
• Lutzke L, Drummond C, Slovic P, Árvai J. Priming critical thinking: simple interventions limit the influence of fake news about climate change on Facebook. Glob Environ Chang. 2019; 58 :101964. doi: 10.1016/j.gloenvcha.2019.101964. [ CrossRef ] [ Google Scholar ]
• Maertens R, Anseel F, van der Linden S. Combatting climate change misinformation: evidence for longevity of inoculation and consensus messaging effects. J Environ Psychol. 2020; 70 :101455. doi: 10.1016/j.jenvp.2020.101455. [ CrossRef ] [ Google Scholar ]
• Mahabub A. A robust technique of fake news detection using ensemble voting classifier and comparison with other classifiers. SN Applied Sciences. 2020; 2 (4):1–9. doi: 10.1007/s42452-020-2326-y. [ CrossRef ] [ Google Scholar ]
• Mahbub S, Pardede E, Kayes A, Rahayu W. Controlling astroturfing on the internet: a survey on detection techniques and research challenges. Int J Web Grid Serv. 2019; 15 (2):139–158. doi: 10.1504/IJWGS.2019.099561. [ CrossRef ] [ Google Scholar ]
• Marsden C, Meyer T, Brown I. Platform values and democratic elections: how can the law regulate digital disinformation? Comput Law Secur Rev. 2020; 36 :105373. doi: 10.1016/j.clsr.2019.105373. [ CrossRef ] [ Google Scholar ]
• Masciari E, Moscato V, Picariello A, Sperlí G (2020) Detecting fake news by image analysis. In: Proceedings of the 24th symposium on international database engineering and applications, pp 1–5. 10.1145/3410566.3410599
• Mazzeo V, Rapisarda A. Investigating fake and reliable news sources using complex networks analysis. Front Phys. 2022; 10 :886544. doi: 10.3389/fphy.2022.886544. [ CrossRef ] [ Google Scholar ]
• McGrew S. Learning to evaluate: an intervention in civic online reasoning. Comput Educ. 2020; 145 :103711. doi: 10.1016/j.compedu.2019.103711. [ CrossRef ] [ Google Scholar ]
• McGrew S, Breakstone J, Ortega T, Smith M, Wineburg S. Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory Res Soc Educ. 2018; 46 (2):165–193. doi: 10.1080/00933104.2017.1416320. [ CrossRef ] [ Google Scholar ]
• Meel P, Vishwakarma DK. Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-arts, challenges and opportunities. Expert Syst Appl. 2020; 153 :112986. doi: 10.1016/j.eswa.2019.112986. [ CrossRef ] [ Google Scholar ]
• Meese J, Frith J, Wilken R. Covid-19, 5G conspiracies and infrastructural futures. Media Int Aust. 2020; 177 (1):30–46. doi: 10.1177/1329878X20952165. [ CrossRef ] [ Google Scholar ]
• Metzger MJ, Hartsell EH, Flanagin AJ. Cognitive dissonance or credibility? A comparison of two theoretical explanations for selective exposure to partisan news. Commun Res. 2020; 47 (1):3–28. doi: 10.1177/0093650215613136. [ CrossRef ] [ Google Scholar ]
• Micallef N, He B, Kumar S, Ahamad M, Memon N (2020) The role of the crowd in countering misinformation: a case study of the Covid-19 infodemic. arXiv preprint arXiv:2011.05773
• Mihailidis P, Viotty S. Spreadable spectacle in digital culture: civic expression, fake news, and the role of media literacies in “post-fact society. Am Behav Sci. 2017; 61 (4):441–454. doi: 10.1177/0002764217701217. [ CrossRef ] [ Google Scholar ]
• Mishra R (2020) Fake news detection using higher-order user to user mutual-attention progression in propagation paths. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 652–653
• Mishra S, Shukla P, Agarwal R. Analyzing machine learning enabled fake news detection techniques for diversified datasets. Wirel Commun Mobile Comput. 2022 doi: 10.1155/2022/1575365. [ CrossRef ] [ Google Scholar ]
• Molina MD, Sundar SS, Le T, Lee D. “Fake news” is not simply false information: a concept explication and taxonomy of online content. Am Behav Sci. 2021; 65 (2):180–212. doi: 10.1177/0002764219878224. [ CrossRef ] [ Google Scholar ]
• Moro C, Birt JR (2022) Review bombing is a dirty practice, but research shows games do benefit from online feedback. Conversation. https://research.bond.edu.au/en/publications/review-bombing-is-a-dirty-practice-but-research-shows-games-do-be
• Mustafaraj E, Metaxas PT (2017) The fake news spreading plague: was it preventable? In: Proceedings of the 2017 ACM on web science conference, pp 235–239. 10.1145/3091478.3091523
• Nagel TW. Measuring fake news acumen using a news media literacy instrument. J Media Liter Educ. 2022; 14 (1):29–42. doi: 10.23860/JMLE-2022-14-1-3. [ CrossRef ] [ Google Scholar ]
• Nakov P (2020) Can we spot the “fake news” before it was even written? arXiv preprint arXiv:2008.04374
• Nekmat E. Nudge effect of fact-check alerts: source influence and media skepticism on sharing of news misinformation in social media. Soc Media Soc. 2020 doi: 10.1177/2056305119897322. [ CrossRef ] [ Google Scholar ]
• Nygren T, Brounéus F, Svensson G. Diversity and credibility in young people’s news feeds: a foundation for teaching and learning citizenship in a digital era. J Soc Sci Educ. 2019; 18 (2):87–109. doi: 10.4119/jsse-917. [ CrossRef ] [ Google Scholar ]
• Nyhan B, Reifler J. Displacing misinformation about events: an experimental test of causal corrections. J Exp Polit Sci. 2015; 2 (1):81–93. doi: 10.1017/XPS.2014.22. [ CrossRef ] [ Google Scholar ]
• Nyhan B, Porter E, Reifler J, Wood TJ. Taking fact-checks literally but not seriously? The effects of journalistic fact-checking on factual beliefs and candidate favorability. Polit Behav. 2020; 42 (3):939–960. doi: 10.1007/s11109-019-09528-x. [ CrossRef ] [ Google Scholar ]
• Nyow NX, Chua HN (2019) Detecting fake news with tweets’ properties. In: 2019 IEEE conference on application, information and network security (AINS), IEEE, pp 24–29. 10.1109/AINS47559.2019.8968706
• Ochoa IS, de Mello G, Silva LA, Gomes AJ, Fernandes AM, Leithardt VRQ (2019) Fakechain: a blockchain architecture to ensure trust in social media networks. In: International conference on the quality of information and communications technology. Springer, Berlin, pp 105–118. 10.1007/978-3-030-29238-6_8
• Ozbay FA, Alatas B. Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A. 2020; 540 :123174. doi: 10.1016/j.physa.2019.123174. [ CrossRef ] [ Google Scholar ]
• Ozturk P, Li H, Sakamoto Y (2015) Combating rumor spread on social media: the effectiveness of refutation and warning. In: 2015 48th Hawaii international conference on system sciences, IEEE, pp 2406–2414. 10.1109/HICSS.2015.288
• Parikh SB, Atrey PK (2018) Media-rich fake news detection: a survey. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR), IEEE, pp 436–441.10.1109/MIPR.2018.00093
• Parrish K (2018) Deep learning & machine learning: what’s the difference? Online: https://parsers.me/deep-learning-machine-learning-whats-the-difference/ . Accessed 20 May 2020
• Paschen J. Investigating the emotional appeal of fake news using artificial intelligence and human contributions. J Prod Brand Manag. 2019; 29 (2):223–233. doi: 10.1108/JPBM-12-2018-2179. [ CrossRef ] [ Google Scholar ]
• Pathak A, Srihari RK (2019) Breaking! Presenting fake news corpus for automated fact checking. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp 357–362
• Peng J, Detchon S, Choo KKR, Ashman H. Astroturfing detection in social media: a binary n-gram-based approach. Concurr Comput: Pract Exp. 2017; 29 (17):e4013. doi: 10.1002/cpe.4013. [ CrossRef ] [ Google Scholar ]
• Pennycook G, Rand DG. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc Natl Acad Sci. 2019; 116 (7):2521–2526. doi: 10.1073/pnas.1806781116. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pennycook G, Rand DG. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. J Pers. 2020; 88 (2):185–200. doi: 10.1111/jopy.12476. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pennycook G, Bear A, Collins ET, Rand DG. The implied truth effect: attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings. Manag Sci. 2020; 66 (11):4944–4957. doi: 10.1287/mnsc.2019.3478. [ CrossRef ] [ Google Scholar ]
• Pennycook G, McPhetres J, Zhang Y, Lu JG, Rand DG. Fighting Covid-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci. 2020; 31 (7):770–780. doi: 10.1177/0956797620939054. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Potthast M, Kiesel J, Reinartz K, Bevendorff J, Stein B (2017) A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638
• Previti M, Rodriguez-Fernandez V, Camacho D, Carchiolo V, Malgeri M (2020) Fake news detection using time series and user features classification. In: International conference on the applications of evolutionary computation (Part of EvoStar), Springer, Berlin, pp 339–353. 10.1007/978-3-030-43722-0_22
• Przybyla P (2020) Capturing the style of fake news. In: Proceedings of the AAAI conference on artificial intelligence, pp 490–497. 10.1609/aaai.v34i01.5386
• Qayyum A, Qadir J, Janjua MU, Sher F. Using blockchain to rein in the new post-truth world and check the spread of fake news. IT Prof. 2019; 21 (4):16–24. doi: 10.1109/MITP.2019.2910503. [ CrossRef ] [ Google Scholar ]
• Qian F, Gong C, Sharma K, Liu Y (2018) Neural user response generator: fake news detection with collective user intelligence. In: IJCAI, vol 18, pp 3834–3840. 10.24963/ijcai.2018/533
• Raza S, Ding C. Fake news detection based on news content and social contexts: a transformer-based approach. Int J Data Sci Anal. 2022; 13 (4):335–362. doi: 10.1007/s41060-021-00302-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ricard J, Medeiros J (2020) Using misinformation as a political weapon: Covid-19 and Bolsonaro in Brazil. Harv Kennedy School misinformation Rev 1(3). https://misinforeview.hks.harvard.edu/article/using-misinformation-as-a-political-weapon-covid-19-and-bolsonaro-in-brazil/
• Roozenbeek J, van der Linden S. Fake news game confers psychological resistance against online misinformation. Palgrave Commun. 2019; 5 (1):1–10. doi: 10.1057/s41599-019-0279-9. [ CrossRef ] [ Google Scholar ]
• Roozenbeek J, van der Linden S, Nygren T. Prebunking interventions based on the psychological theory of “inoculation” can reduce susceptibility to misinformation across cultures. Harv Kennedy School Misinformation Rev. 2020 doi: 10.37016//mr-2020-008. [ CrossRef ] [ Google Scholar ]
• Roozenbeek J, Schneider CR, Dryhurst S, Kerr J, Freeman AL, Recchia G, Van Der Bles AM, Van Der Linden S. Susceptibility to misinformation about Covid-19 around the world. R Soc Open Sci. 2020; 7 (10):201199. doi: 10.1098/rsos.201199. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rubin VL, Conroy N, Chen Y, Cornwell S (2016) Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the second workshop on computational approaches to deception detection, pp 7–17
• Ruchansky N, Seo S, Liu Y (2017) Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 797–806. 10.1145/3132847.3132877
• Schuyler AJ (2019) Regulating facts: a procedural framework for identifying, excluding, and deterring the intentional or knowing proliferation of fake news online. Univ Ill JL Technol Pol’y, vol 2019, pp 211–240
• Shae Z, Tsai J (2019) AI blockchain platform for trusting news. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS), IEEE, pp 1610–1619. 10.1109/ICDCS.2019.00160
• Shang W, Liu M, Lin W, Jia M (2018) Tracing the source of news based on blockchain. In: 2018 IEEE/ACIS 17th international conference on computer and information science (ICIS), IEEE, pp 377–381. 10.1109/ICIS.2018.8466516
• Shao C, Ciampaglia GL, Flammini A, Menczer F (2016) Hoaxy: A platform for tracking online misinformation. In: Proceedings of the 25th international conference companion on world wide web, pp 745–750. 10.1145/2872518.2890098
• Shao C, Ciampaglia GL, Varol O, Yang KC, Flammini A, Menczer F. The spread of low-credibility content by social bots. Nat Commun. 2018; 9 (1):1–9. doi: 10.1038/s41467-018-06930-7. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shao C, Hui PM, Wang L, Jiang X, Flammini A, Menczer F, Ciampaglia GL. Anatomy of an online misinformation network. PLoS ONE. 2018; 13 (4):e0196087. doi: 10.1371/journal.pone.0196087. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sharma K, Qian F, Jiang H, Ruchansky N, Zhang M, Liu Y. Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol (TIST) 2019; 10 (3):1–42. doi: 10.1145/3305260. [ CrossRef ] [ Google Scholar ]
• Sharma K, Seo S, Meng C, Rambhatla S, Liu Y (2020) Covid-19 on social media: analyzing misinformation in Twitter conversations. arXiv preprint arXiv:2003.12309
• Shen C, Kasra M, Pan W, Bassett GA, Malloch Y, O’Brien JF. Fake images: the effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New Media Soc. 2019; 21 (2):438–463. doi: 10.1177/1461444818799526. [ CrossRef ] [ Google Scholar ]
• Sherman IN, Redmiles EM, Stokes JW (2020) Designing indicators to combat fake media. arXiv preprint arXiv:2010.00544
• Shi P, Zhang Z, Choo KKR. Detecting malicious social bots based on clickstream sequences. IEEE Access. 2019; 7 :28855–28862. doi: 10.1109/ACCESS.2019.2901864. [ CrossRef ] [ Google Scholar ]
• Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newsl. 2017; 19 (1):22–36. doi: 10.1145/3137597.3137600. [ CrossRef ] [ Google Scholar ]
• Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2018a) Fakenewsnet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv preprint arXiv:1809.01286 , 10.1089/big.2020.0062 [ PubMed ]
• Shu K, Wang S, Liu H (2018b) Understanding user profiles on social media for fake news detection. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR), IEEE, pp 430–435. 10.1109/MIPR.2018.00092
• Shu K, Wang S, Liu H (2019a) Beyond news contents: the role of social context for fake news detection. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 312–320. 10.1145/3289600.3290994
• Shu K, Zhou X, Wang S, Zafarani R, Liu H (2019b) The role of user profiles for fake news detection. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp 436–439. 10.1145/3341161.3342927
• Shu K, Bhattacharjee A, Alatawi F, Nazer TH, Ding K, Karami M, Liu H. Combating disinformation in a social media age. Wiley Interdiscip Rev: Data Min Knowl Discov. 2020; 10 (6):e1385. doi: 10.1002/widm.1385. [ CrossRef ] [ Google Scholar ]
• Shu K, Mahudeswaran D, Wang S, Liu H. Hierarchical propagation networks for fake news detection: investigation and exploitation. Proc Int AAAI Conf Web Soc Media AAAI Press. 2020; 14 :626–637. [ Google Scholar ]
• Shu K, Wang S, Lee D, Liu H (2020c) Mining disinformation and fake news: concepts, methods, and recent advancements. In: Disinformation, misinformation, and fake news in social media. Springer, Berlin, pp 1–19 10.1007/978-3-030-42699-6_1
• Shu K, Zheng G, Li Y, Mukherjee S, Awadallah AH, Ruston S, Liu H (2020d) Early detection of fake news with multi-source weak social supervision. In: ECML/PKDD (3), pp 650–666
• Singh VK, Ghosh I, Sonagara D. Detecting fake news stories via multimodal analysis. J Am Soc Inf Sci. 2021; 72 (1):3–17. doi: 10.1002/asi.24359. [ CrossRef ] [ Google Scholar ]
• Sintos S, Agarwal PK, Yang J (2019) Selecting data to clean for fact checking: minimizing uncertainty vs. maximizing surprise. Proc VLDB Endowm 12(13), 2408–2421. 10.14778/3358701.3358708 [ CrossRef ]
• Snow J (2017) Can AI win the war against fake news? MIT Technology Review Online: https://www.technologyreview.com/s/609717/can-ai-win-the-war-against-fake-news/ . Accessed 3 Oct. 2020
• Song G, Kim S, Hwang H, Lee K (2019) Blockchain-based notarization for social media. In: 2019 IEEE international conference on consumer clectronics (ICCE), IEEE, pp 1–2 10.1109/ICCE.2019.8661978
• Starbird K, Arif A, Wilson T (2019) Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations. In: Proceedings of the ACM on human–computer interaction, vol 3(CSCW), pp 1–26 10.1145/3359229
• Sterret D, Malato D, Benz J, Kantor L, Tompson T, Rosenstiel T, Sonderman J, Loker K, Swanson E (2018) Who shared it? How Americans decide what news to trust on social media. Technical report, Norc Working Paper Series, WP-2018-001, pp 1–24
• Sutton RM, Douglas KM. Conspiracy theories and the conspiracy mindset: implications for political ideology. Curr Opin Behav Sci. 2020; 34 :118–122. doi: 10.1016/j.cobeha.2020.02.015. [ CrossRef ] [ Google Scholar ]
• Tandoc EC, Jr, Thomas RJ, Bishop L. What is (fake) news? Analyzing news values (and more) in fake stories. Media Commun. 2021; 9 (1):110–119. doi: 10.17645/mac.v9i1.3331. [ CrossRef ] [ Google Scholar ]
• Tchakounté F, Faissal A, Atemkeng M, Ntyam A. A reliable weighting scheme for the aggregation of crowd intelligence to detect fake news. Information. 2020; 11 (6):319. doi: 10.3390/info11060319. [ CrossRef ] [ Google Scholar ]
• Tchechmedjiev A, Fafalios P, Boland K, Gasquet M, Zloch M, Zapilko B, Dietze S, Todorov K (2019) Claimskg: a knowledge graph of fact-checked claims. In: International semantic web conference. Springer, Berlin, pp 309–324 10.1007/978-3-030-30796-7_20
• Treen KMd, Williams HT, O’Neill SJ. Online misinformation about climate change. Wiley Interdiscip Rev Clim Change. 2020; 11 (5):e665. doi: 10.1002/wcc.665. [ CrossRef ] [ Google Scholar ]
• Tsang SJ. Motivated fake news perception: the impact of news sources and policy support on audiences’ assessment of news fakeness. J Mass Commun Q. 2020 doi: 10.1177/1077699020952129. [ CrossRef ] [ Google Scholar ]
• Tschiatschek S, Singla A, Gomez Rodriguez M, Merchant A, Krause A (2018) Fake news detection in social networks via crowd signals. In: Companion proceedings of the the web conference 2018, pp 517–524. 10.1145/3184558.3188722
• Uppada SK, Manasa K, Vidhathri B, Harini R, Sivaselvan B. Novel approaches to fake news and fake account detection in OSNS: user social engagement and visual content centric model. Soc Netw Anal Min. 2022; 12 (1):1–19. doi: 10.1007/s13278-022-00878-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van der Linden S, Roozenbeek J (2020) Psychological inoculation against fake news. In: Accepting, sharing, and correcting misinformation, the psychology of fake news. 10.4324/9780429295379-11
• Van der Linden S, Panagopoulos C, Roozenbeek J. You are fake news: political bias in perceptions of fake news. Media Cult Soc. 2020; 42 (3):460–470. doi: 10.1177/0163443720906992. [ CrossRef ] [ Google Scholar ]
• Valenzuela S, Muñiz C, Santos M. Social media and belief in misinformation in mexico: a case of maximal panic, minimal effects? Int J Press Polit. 2022 doi: 10.1177/19401612221088988. [ CrossRef ] [ Google Scholar ]
• Vasu N, Ang B, Teo TA, Jayakumar S, Raizal M, Ahuja J (2018) Fake news: national security in the post-truth era. RSIS
• Vereshchaka A, Cosimini S, Dong W (2020) Analyzing and distinguishing fake and real news to mitigate the problem of disinformation. In: Computational and mathematical organization theory, pp 1–15. 10.1007/s10588-020-09307-8
• Verstraete M, Bambauer DE, Bambauer JR (2017) Identifying and countering fake news. Arizona legal studies discussion paper 73(17-15). 10.2139/ssrn.3007971
• Vilmer J, Escorcia A, Guillaume M, Herrera J (2018) Information manipulation: a challenge for our democracies. In: Report by the Policy Planning Staff (CAPS) of the ministry for europe and foreign affairs, and the institute for strategic research (RSEM) of the Ministry for the Armed Forces
• Vishwakarma DK, Varshney D, Yadav A. Detection and veracity analysis of fake news via scrapping and authenticating the web search. Cogn Syst Res. 2019; 58 :217–229. doi: 10.1016/j.cogsys.2019.07.004. [ CrossRef ] [ Google Scholar ]
• Vlachos A, Riedel S (2014) Fact checking: task definition and dataset construction. In: Proceedings of the ACL 2014 workshop on language technologies and computational social science, pp 18–22. 10.3115/v1/W14-2508
• von der Weth C, Abdul A, Fan S, Kankanhalli M (2020) Helping users tackle algorithmic threats on social media: a multimedia research agenda. In: Proceedings of the 28th ACM international conference on multimedia, pp 4425–4434. 10.1145/3394171.3414692
• Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018; 359 (6380):1146–1151. doi: 10.1126/science.aap9559. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vraga EK, Bode L. Using expert sources to correct health misinformation in social media. Sci Commun. 2017; 39 (5):621–645. doi: 10.1177/1075547017731776. [ CrossRef ] [ Google Scholar ]
• Waldman AE. The marketplace of fake news. Univ Pa J Const Law. 2017; 20 :845. [ Google Scholar ]
• Wang WY (2017) “Liar, liar pants on fire”: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648
• Wang L, Wang Y, de Melo G, Weikum G. Understanding archetypes of fake news via fine-grained classification. Soc Netw Anal Min. 2019; 9 (1):1–17. doi: 10.1007/s13278-019-0580-z. [ CrossRef ] [ Google Scholar ]
• Wang Y, Han H, Ding Y, Wang X, Liao Q (2019b) Learning contextual features with multi-head self-attention for fake news detection. In: International conference on cognitive computing. Springer, Berlin, pp 132–142. 10.1007/978-3-030-23407-2_11
• Wang Y, McKee M, Torbica A, Stuckler D. Systematic literature review on the spread of health-related misinformation on social media. Soc Sci Med. 2019; 240 :112552. doi: 10.1016/j.socscimed.2019.112552. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang Y, Yang W, Ma F, Xu J, Zhong B, Deng Q, Gao J (2020) Weak supervision for fake news detection via reinforcement learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 516–523. 10.1609/aaai.v34i01.5389
• Wardle C (2017) Fake news. It’s complicated. Online: https://medium.com/1st-draft/fake-news-its-complicated-d0f773766c79 . Accessed 3 Oct 2020
• Wardle C. The need for smarter definitions and practical, timely empirical research on information disorder. Digit J. 2018; 6 (8):951–963. doi: 10.1080/21670811.2018.1502047. [ CrossRef ] [ Google Scholar ]
• Wardle C, Derakhshan H. Information disorder: toward an interdisciplinary framework for research and policy making. Council Eur Rep. 2017; 27 :1–107. [ Google Scholar ]
• Weiss AP, Alwan A, Garcia EP, Garcia J. Surveying fake news: assessing university faculty’s fragmented definition of fake news and its impact on teaching critical thinking. Int J Educ Integr. 2020; 16 (1):1–30. doi: 10.1007/s40979-019-0049-x. [ CrossRef ] [ Google Scholar ]
• Wu L, Liu H (2018) Tracing fake-news footprints: characterizing social media messages by how they propagate. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 637–645. 10.1145/3159652.3159677
• Wu L, Rao Y (2020) Adaptive interaction fusion networks for fake news detection. arXiv preprint arXiv:2004.10009
• Wu L, Morstatter F, Carley KM, Liu H. Misinformation in social media: definition, manipulation, and detection. ACM SIGKDD Explor Newsl. 2019; 21 (2):80–90. doi: 10.1145/3373464.3373475. [ CrossRef ] [ Google Scholar ]
• Wu Y, Ngai EW, Wu P, Wu C. Fake news on the internet: a literature review, synthesis and directions for future research. Intern Res. 2022 doi: 10.1108/INTR-05-2021-0294. [ CrossRef ] [ Google Scholar ]
• Xu K, Wang F, Wang H, Yang B. Detecting fake news over online social media via domain reputations and content understanding. Tsinghua Sci Technol. 2019; 25 (1):20–27. doi: 10.26599/TST.2018.9010139. [ CrossRef ] [ Google Scholar ]
• Yang F, Pentyala SK, Mohseni S, Du M, Yuan H, Linder R, Ragan ED, Ji S, Hu X (2019a) Xfake: explainable fake news detector with visualizations. In: The world wide web conference, pp 3600–3604. 10.1145/3308558.3314119
• Yang X, Li Y, Lyu S (2019b) Exposing deep fakes using inconsistent head poses. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 8261–8265. 10.1109/ICASSP.2019.8683164
• Yaqub W, Kakhidze O, Brockman ML, Memon N, Patil S (2020) Effects of credibility indicators on social media news sharing intent. In: Proceedings of the 2020 CHI conference on human factors in computing systems, pp 1–14. 10.1145/3313831.3376213
• Yavary A, Sajedi H, Abadeh MS. Information verification in social networks based on user feedback and news agencies. Soc Netw Anal Min. 2020; 10 (1):1–8. doi: 10.1007/s13278-019-0616-4. [ CrossRef ] [ Google Scholar ]
• Yazdi KM, Yazdi AM, Khodayi S, Hou J, Zhou W, Saedy S. Improving fake news detection using k-means and support vector machine approaches. Int J Electron Commun Eng. 2020; 14 (2):38–42. doi: 10.5281/zenodo.3669287. [ CrossRef ] [ Google Scholar ]
• Zannettou S, Sirivianos M, Blackburn J, Kourtellis N. The web of false information: rumors, fake news, hoaxes, clickbait, and various other shenanigans. J Data Inf Qual (JDIQ) 2019; 11 (3):1–37. doi: 10.1145/3309699. [ CrossRef ] [ Google Scholar ]
• Zellers R, Holtzman A, Rashkin H, Bisk Y, Farhadi A, Roesner F, Choi Y (2019) Defending against neural fake news. arXiv preprint arXiv:1905.12616
• Zhang X, Ghorbani AA. An overview of online fake news: characterization, detection, and discussion. Inf Process Manag. 2020; 57 (2):102025. doi: 10.1016/j.ipm.2019.03.004. [ CrossRef ] [ Google Scholar ]
• Zhang J, Dong B, Philip SY (2020) Fakedetector: effective fake news detection with deep diffusive neural network. In: 2020 IEEE 36th international conference on data engineering (ICDE), IEEE, pp 1826–1829. 10.1109/ICDE48307.2020.00180
• Zhang Q, Lipani A, Liang S, Yilmaz E (2019a) Reply-aided detection of misinformation via Bayesian deep learning. In: The world wide web conference, pp 2333–2343. 10.1145/3308558.3313718
• Zhang X, Karaman S, Chang SF (2019b) Detecting and simulating artifacts in GAN fake images. In: 2019 IEEE international workshop on information forensics and security (WIFS), IEEE, pp 1–6 10.1109/WIFS47025.2019.9035107
• Zhou X, Zafarani R. A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv (CSUR) 2020; 53 (5):1–40. doi: 10.1145/3395046. [ CrossRef ] [ Google Scholar ]
• Zubiaga A, Aker A, Bontcheva K, Liakata M, Procter R. Detection and resolution of rumours in social media: a survey. ACM Comput Surv (CSUR) 2018; 51 (2):1–36. doi: 10.1145/3161603. [ CrossRef ] [ Google Scholar ]

Essay on Effect of Fake News on Social Media

Students are often asked to write an essay on Effect of Fake News on Social Media in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Effect of Fake News on Social Media

Introduction.

Fake news refers to misinformation or false stories spread on social media. It’s a serious problem affecting the credibility of information online.

Impact on Society

Fake news can cause panic and confusion. It can influence public opinion, leading to harmful decisions or actions.

Role of Social Media

Social media platforms unknowingly promote fake news due to their algorithms, which favour engaging content, whether true or false.

To combat fake news, it’s crucial to verify information before sharing and report suspected fake news to social media platforms.

250 Words Essay on Effect of Fake News on Social Media

Fake news, the deliberate spread of false information, has emerged as a significant issue in the digital age. Social media platforms, with their vast reach and rapid information dissemination, have become a fertile ground for fake news propagation.

The Proliferation of Fake News

The accessibility and anonymity of social media platforms make them a convenient tool for spreading fake news. Misinformation can be created and shared with a few clicks, reaching millions within seconds. The lack of stringent fact-checking mechanisms further exacerbates this issue.

Impacts on Public Perception and Behavior

Fake news can distort public perception, fueling fear, bias, and misunderstanding. It can influence political views, incite violence, and even impact public health decisions, as seen during the COVID-19 pandemic.

The Role of Algorithms

Social media algorithms, designed to prioritize engaging content, often inadvertently promote fake news. These algorithms, based on user behavior, can create echo chambers, reinforcing and amplifying misinformation.

The effect of fake news on social media is profound and multifaceted. It underscores the urgent need for improved digital literacy, robust fact-checking mechanisms, and algorithmic transparency. As we move further into the digital age, these issues will require ongoing attention and innovative solutions.

500 Words Essay on Effect of Fake News on Social Media

Fake news, a term frequently used in recent years, refers to fabricated information that mimics news media content in form but not in organizational process or intent. The proliferation of fake news, especially on social media, has become a significant concern due to its potential to manipulate public opinion, incite hatred, and even influence elections. This essay explores the effects of fake news on social media, focusing on its implications for society, politics, and the economy.

The Social Impact of Fake News

Fake news on social media has a profound social impact. It can manipulate people’s perceptions, leading to misinformation and the spread of fear and panic. For instance, during the COVID-19 pandemic, the dissemination of false information about the virus created unnecessary panic and confusion. Furthermore, fake news can exacerbate social divisions by exploiting existing biases and prejudices. By creating an echo chamber effect, social media platforms reinforce these biases, leading to increased polarization and social conflict.

Political Consequences of Fake News

The political implications of fake news are equally alarming. By manipulating public opinion, fake news can influence electoral outcomes and undermine democratic processes. The 2016 US elections are a case in point, where fake news played a significant role in shaping public opinion. Moreover, fake news can erode trust in institutions and leaders by spreading conspiracy theories and misinformation. This distrust can destabilize political systems and lead to social unrest.

Economic Implications of Fake News

The economic effects of fake news are often overlooked but can be substantial. Fake news can influence stock markets, as investors may make decisions based on false information. Additionally, companies can suffer reputational damage due to fake news, leading to financial losses. Furthermore, the resources spent on combating fake news represent a significant economic cost.

In conclusion, the impact of fake news on social media is far-reaching, affecting society, politics, and the economy. It is essential to combat this phenomenon through media literacy education, fact-checking, and the responsible use of social media platforms. As we move further into the digital age, the fight against fake news will continue to be a critical challenge that needs collective effort and vigilance.

That’s it! I hope the essay helped you.

If you’re looking for more, here are essays on other interesting topics:

• Essay on Ecotourism
• Essay on Ecosystem Restoration
• Essay on Economics Extended

Apart from these, you can look at all the essays by clicking here .

Happy studying!

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

What are you looking for?

The researchers sought to understand how the reward structure of social media sites drives users to develop habits of posting misinformation on social media. (Photo/AdobeStock)

USC study reveals the key reason why fake news spreads on social media

The USC-led study of more than 2,400 Facebook users suggests that platforms — more than individual users — have a larger role to play in stopping the spread of misinformation online.

USC researchers may have found the biggest influencer in the spread of fake news: social platforms’ structure of rewarding users for habitually sharing information.

The team’s findings, published Monday by Proceedings of the National Academy of Sciences , upend popular misconceptions that misinformation spreads because users lack the critical thinking skills necessary for discerning truth from falsehood or because their strong political beliefs skew their judgment.

Just 15% of the most habitual news sharers in the research were responsible for spreading about 30% to 40% of the fake news.

The research team from the USC Marshall School of Business and the USC Dornsife College of Letters, Arts and Sciences wondered: What motivates these users? As it turns out, much like any video game, social media has a rewards system that encourages users to stay on their accounts and keep posting and sharing. Users who post and share frequently, especially sensational, eye-catching information, are likely to attract attention.

“Due to the reward-based learning systems on social media, users form habits of sharing information that gets recognition from others,” the researchers wrote. “Once habits form, information sharing is automatically activated by cues on the platform without users considering critical response outcomes, such as spreading misinformation.”

Posting, sharing and engaging with others on social media can, therefore, become a habit.

“[Misinformation is] really a function of the structure of the social media sites themselves.” — Wendy Wood , USC expert on habits

“Our findings show that misinformation isn’t spread through a deficit of users. It’s really a function of the structure of the social media sites themselves,” said Wendy Wood , an expert on habits and USC emerita Provost Professor of psychology and business.

“The habits of social media users are a bigger driver of misinformation spread than individual attributes. We know from prior research that some people don’t process information critically, and others form opinions based on political biases, which also affects their ability to recognize false stories online,” said Gizem Ceylan, who led the study during her doctorate at USC Marshall and is now a postdoctoral researcher at the Yale School of Management . “However, we show that the reward structure of social media platforms plays a bigger role when it comes to misinformation spread.”

In a novel approach, Ceylan and her co-authors sought to understand how the reward structure of social media sites drives users to develop habits of posting misinformation on social media.

Why fake news spreads: behind the social network

Overall, the study involved 2,476 active Facebook users ranging in age from 18 to 89 who volunteered in response to online advertising to participate. They were compensated to complete a “decision-making” survey approximately seven minutes long.

Surprisingly, the researchers found that users’ social media habits doubled and, in some cases, tripled the amount of fake news they shared. Their habits were more influential in sharing fake news than other factors, including political beliefs and lack of critical reasoning.

Frequent, habitual users forwarded six times more fake news than occasional or new users.

“This type of behavior has been rewarded in the past by algorithms that prioritize engagement when selecting which posts users see in their news feed, and by the structure and design of the sites themselves,” said second author Ian A. Anderson , a behavioral scientist and doctoral candidate at USC Dornsife. “Understanding the dynamics behind misinformation spread is important given its political, health and social consequences.”

Experimenting with different scenarios to see why fake news spreads

In the first experiment, the researchers found that habitual users of social media share both true and fake news.

In another experiment, the researchers found that habitual sharing of misinformation is part of a broader pattern of insensitivity to the information being shared. In fact, habitual users shared politically discordant news — news that challenged their political beliefs — as much as concordant news that they endorsed.

Lastly, the team tested whether social media reward structures could be devised to promote sharing of true over false information. They showed that incentives for accuracy rather than popularity (as is currently the case on social media sites) doubled the amount of accurate news that users share on social platforms.

The study’s conclusions:

• Habitual sharing of misinformation is not inevitable.
• Users could be incentivized to build sharing habits that make them more sensitive to sharing truthful content.
• Effectively reducing misinformation would require restructuring the online environments that promote and support its sharing.

These findings suggest that social media platforms can take a more active step than moderating what information is posted and instead pursue structural changes in their reward structure to limit the spread of misinformation.

About the study:  The research was supported and funded by the USC Dornsife College of Letters, Arts and Sciences Department of Psychology, the USC Marshall School of Business and the Yale University School of Management.

Related Articles

Transforming waste materials into useful products, exactly what opportunities does quantum computing present, anyway, the best place to see the total solar eclipse in 2024.

Fake news on Social Media: the Impact on Society

• Open access
• Published: 19 January 2022

Cite this article

You have full access to this open access article

• Femi Olan   ORCID: orcid.org/0000-0002-7377-9882 1 ,
• Uchitha Jayawickrama 2 ,
• Emmanuel Ogiemwonyi Arakpogun 1 ,
• Jana Suklan 3 &
• Shaofeng Liu 4  

175k Accesses

35 Citations

37 Altmetric

Explore all metrics

Fake news (FN) on social media (SM) rose to prominence in 2016 during the United States of America presidential election, leading people to question science, true news (TN), and societal norms. FN is increasingly affecting societal values, changing opinions on critical issues and topics as well as redefining facts, truths, and beliefs. To understand the degree to which FN has changed society and the meaning of FN, this study proposes a novel conceptual framework derived from the literature on FN, SM, and societal acceptance theory. The conceptual framework is developed into a meta-framework that analyzes survey data from 356 respondents. This study explored fuzzy set-theoretic comparative analysis; the outcomes of this research suggest that societies are split on differentiating TN from FN. The results also show splits in societal values. Overall, this study provides a new perspective on how FN on SM is disintegrating societies and replacing TN with FN.

Similar content being viewed by others

Advances in social media research: past, present and future.

Kawaljeet Kaur Kapoor, Kuttimani Tamilmani, … Sridhar Nerur

Media and Stereotypes

The disaster of misinformation: a review of research in social media

Sadiq Muhammed T & Saji K. Mathew

Avoid common mistakes on your manuscript.

1 Introduction

In cascading news and sensitive information, the fundamental principles are embedded in the concepts of truth as well as the theories of accuracy in communication (Brennen, 2017 ; Dwivedi et al., 2018 ; Orso et al., 2020 ; Pennycook et al., 2020 ). However, in the past five years or so, social media (SM) has redefined the structure, dimensions, and complexity of the news (Berkowitz & Schwartz, 2016 ; Copeland, 2007 ; Kim & Lyon, 2014 ). The impact of SM, specifically on political affairs, has been attracting more interest, as SM platforms, notably Twitter, Facebook, and Instagram, enable the broad sharing of information and news (Vosoughi et al., 2018 ). In addition to providing information, another main purpose of SM is to enable people to engage in social interaction, communication, and entertainment (Hwang et al., 2011 ; Kuem et al., 2017 ). In particular, many SM posts are looking for support, where reposting aims to spread messages via the multiplicative effect. Consequently, this study purpose is to address the research problem and gap which suggest that SM platform providers are doing little in tackling the spread and cascading of FN on SM.

By providing unlimited access to a large amount of information, people can share different beliefs and values (George et al., 2018 ; Kim et al., 2019 ; Rubin, 2019 ). However, the risks and implications of this new resource remain unclear to most of the population. One such risk is fake news (FN). FN, although unvetted, has a credible and professional appearance, ensuring that people cannot always distinguish it from true news (TN) (Kumar et al., 2018 ). The effects of FN cut across the society, for example, the spread of FN on SM determines how governments, organizations, and people respond to events in the society. Majority of FN is targeted to a specific sample of the population with the aim of promoting a certain ideology by stimulating strong beliefs and polarizing society (Chen & Sharma, 2015 ). According to Kumar et al. ( 2018 ); Lundmark et al. ( 2017 ); Tandoc et al. ( 2019 ), a periodic review of FN on SM is thus required to limit discord and violence by groups or individuals in society.

FN has become a major part of SM, raising doubts about information credibility, quality, and verification. Studies investigating the influence of FN on SM have appeared in various fields such as digital media, journalism, and politics; however, in-depth analyses of the impact of FN on society remain scarce. Furthermore, despite the growing body of research on FN and SM —a significant factor in the fight against FN —(Tandoc et al., 2018 ), an adequate review of the impact of FN in SM on society is also lacking.

Hence, The aim of this study is to explore the role of SM platform providers in reducing the spread of FN in the society, as the research gap identified from previous studies (Kim & Dennis, 2019 ; Kim et al., 2019 ; Knight & Tsoukas, 2019 ; Roozenbeek & van der Linden, 2019 ) on the limited research on the impact of FN on the society, leading to this study finding answers to the following research questions (RQs):

RQ1. Why is FN cascading impacting negatively on the society?

RQ2. Are the big SM organizations taking actions in reducing FN cascading?

Based on the foregoing, this study provides a holistic view of the three focus areas (FN, SM, and societal acceptance) by reviewing research publications, case studies, and experts’ opinions to produce a conceptual framework, an insightful and comprehensive meta-framework. This study then analyzes the associations among the three distinct fields from theoretical and practical perspectives. These associations derived from the literature are tested using an analytic technique called fuzzy set analysis to show if they are supported, thereby indicating society’s efforts to combat FN. We find that people’s interpretations of what is TN or FN affect societal efforts to reduce the spread of FN.

The findings of this study contribute to research on FN on SM, specifically looking at societal impacts. They provide experts and researchers in these fields with insights into how communities are effectively combating the spread of FN and how to implement the useful ideas from this research to strengthen the inputs in tackling FN on SM. Further, the findings of this research not only provide support for the associations but demonstrate a model for societal strategies to manage the spread of FN as well as fact-checking and information verification, thus equipping society with the tools to recognize the differences between FN and TN.

The remaining sections in this study are organized as follows: the theoretical development of the conceptual meta-framework explains the literature for the concept of FN, SM, and societal acceptance. This is followed by researched method section that describes the data, analysis and presents the results of the study. Further, there is a discussion section on the results, implications of this study for research, practice, and the society, finally limitations and future research.

2 Theoretical Development of the Conceptual Meta-Framework

FN is shaped to replicate TN by mimicking its characteristics (i.e. accuracy, verifiability, brevity, balance, and truthfulness) to mislead the public (Han et al., 2017 ; Kim & Dennis, 2019 ; Kim et al., 2019 ). FN is not a new phenomenon, according to Burkhardt ( 2017 ), FN can be traced back to at least Roman times when the first Roman Emperor had to announce fake news to encourage Octavian to destroy the republican system. During the Roman period, there was no way of verifying and validating the authenticity of news, as challenging authority was classed as treason. The 20th century heralded a new era of numerous one-to-many communication modes such as newspapers, radio stations, and television stations, marking the beginning of misinformation in news (Aggarwal et al., 2012 ; Kim & Dennis, 2019 ; Kim et al., 2019 ; Knight & Tsoukas, 2019 ; Manski, 1993 ; Preti & Miotto, 2011 ; Roozenbeek & van der Linden, 2019 ). With the emergence of multimedia corporations, the content of FN has been gaining new audiences (Oestreicher-Singer & Zalmanson, 2013 ), and the arrival of the Internet towards the end of the century improved the phenomenon of FN (Kapoor et al., 2018 ). As technology advanced in the 21st century, SM arrived, multiplying the dissemination of FN using both one-to-many and many-to-many strategies.

2.1 Understanding FN

FN content, which is divided into individual opinions and scientific consensus on trending issues such as COVID-19, evolution, and climate change, has long existed (Knight & Tsoukas, 2019 ). However, constant changes in political strategies have fundamentally impacted how information is defined, viewed, and interpreted at all levels of communication (Massari, 2010 ). Aggarwal and colleagues argued that incorrect scientific, political, and belief-oriented information has significant causes and consequences on individuals that are more politically inclined and those aiming to drive their ideas to wider society (Aggarwal et al., 2012 ). Therefore, individuals actively seeking information are united in their pursuit of knowledge and political action (Aggarwal & Singh, 2013 ). It is impossible to change their values and beliefs, abandon old ways and accept the fact-checked news, new methods to enlightening individuals or people with similar beliefs to adopt new states to a degree of news verification and validation (Cao et al., 2015 ; Centeno et al., 2015 ; Kim & Lyon, 2014 ).

As FN is fundamentally built on untraced and misleading phenomena, experts and researchers have noted a rising interest in the development of fact-checking tools to spot the spread of FN content in society (Berkowitz & Schwartz, 2016 ; Hwang et al., 2011 ; Miranda et al., 2015 ; Miranda et al., 2016 ). However, despite the large investment in innovative tools for identifying, distinguishing, and reducing factual discrepancies (e.g., ‘Content Authentication’ by Adobe for spotting alterations to original content), the challenges concerning the spread of FN remain unresolved, as society continues to engage with, debate, and promote such content (Kwon et al., 2017 ; Pierri et al., 2020 ). Indeed, the gap between fact-checking and the fundamental values and beliefs of the public discourages people from promoting fact-checking rather than accepting the dangers of FN (Kim & Lyon, 2014 ; Lukyanenko et al., 2014 ). Therefore, these tools do little to reduce the spread of FN in practice.

2.2 SM and Society

SM provides an environment in which individuals can exchange personal, group, or popular interests to build relationships with people that have similar and/or diverging beliefs and values. For example, most people of a particular age group share similar interests courtesy of growing up in the same era (Gomez-Miranda et al., 2015 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ). People’s characteristics are often inherited from educational institutions, communities, and family lifestyles (Matook et al., 2015 ). Further, certain age groups continue to hold onto specific values and beliefs, as reflected in the public’s response to the 2016 and 2020 U.S. presidential election and the 2019 UK general election (Prosser et al., 2020 ; Wang et al., 2016 ). Accordingly, Venkatraman et al. ( 2018 ) argued that values and beliefs are passed down through family generations, making it possible for a group in society to continue to hold onto specific philosophies.

SM plays an important role in helping people reconnect with friends and families as well as find jobs and purchase products and services (Kim & Dennis, 2019 ; Leong et al., 2015 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ; Pierri et al., 2020 ). SM platforms are also channels for recruiting interested parties for the continuity and propagation of a long-held ideology. Moreover, people with common demographic attributes use the instant messaging services on SM to communicate more than those without such shared demographics (Baur, 2017 ). SM platforms are thus online services that mirror real-world activities (e.g., dating services from Facebook, live Instagram feeds from parties).

The societal acceptance strategy can reduce the spread of FN (Haigh et al., 2018 ; Lundmark et al., 2017 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ; Sommariva et al., 2018 ). However, the expansion of multiple access points for information and news sharing on SM platforms contributes more to the spread of falsity than reducing its impact. Nevertheless, societal acceptance is considered to be a game-changer for controlling the spread of FN by SM (Egelhofer & Lecheler, 2019 ). Some empirical studies have analyzed the spread and flow of FN online (Garg et al., 2011 ; Gray et al., 2011 ), but little research examines how human judgment can differentiate truth from falsity. To reduce the spread of FN in society, it is important to understand the triangle of FN, the relationships between the constructs from each circle, and the associations that bind the circles, and then analyze the strength of the relationship (Chang et al., 2015 ; Chen & Sharma, 2015 ; Matook et al., 2015 ).

2.3 Meta-framework on the Impact of FN

This study developed a meta-framework based on the literature on FN, SM, and societal acceptance. Each of these perspectives, depicted as circles in the meta-framework, discusses the constructs that contribute to defining the clusters in theory. The constructs that then emerge from each perspective are the foundation for the meta-framework discussing the relationships among their associations. This study further develops notations to define the associations. By combining the three defined circles, these perspectives provide a new theoretical framework, as previous studies have shown that feasibilities to conceptualize phenomenon are at a wide spectrum (Table  1 ).

This study adopted the epidemiological model as a suitable theory for discussing the meta-framework perspectives. In particular, it employed the conceptual model of the disease triangle. In the 1960 s, the disease triangle was developed by George McNew to understand the pathology and epidemiology of plants and their diseases (Scholthof, 2007 ). This model stated that for a disease to manifest, three fundamental elements are required: the environment; the infectious pathogen that carries the virus, bacteria, or other micro-organisms; and the host. In this study, FN is defined as an ‘infectious pathogen’, as it is an epidemic that consists of varieties of fake news (Pan et al., 2017 ). According to Scholthof ( 2007 ), the environment determines whether the infection can be controlled; here, as shown in Fig.  1 , SM is conceptualized as the environment, the hosts are the readers, individuals, and society.

Fake news triangle

SM as an environment for cascading of FN has a structure (Chen et al., 2015 ; Miller & Tucker, 2013 ; Scholthof, 2007 ). The aim of the SM structure is to generate contents that attract millions of views by re-sharing news or information targeting a set of specific viewers. As the contents are shared and attained a viral status in the society, SM organizations are leveraging increased profits (Mettler & Winter, 2016 ). Primarily, SM structure is designed on contents ranking system constructed by algorithm ranking techniques, the method of data management and significance leveling in data priority (Hamamreh & Awad, 2017 ). News and information are ranked in a methodological order that links constructing a natural distribution by connecting between nodes of the SM (Gerlach et al., 2015 ; Matook et al., 2015 ). To understand the ranking system in SM, each node is assigned a unique code by creating iterative process of weights in network, these weights are assigned according to the content structure of the SM node (Brennen, 2017 ; Burkhardt, 2017 ; Chen, 2018 ). According to Brennen ( 2017 ); Burkhardt ( 2017 ); Chang et al. ( 2014 ); Chen ( 2018 ); Maier et al. ( 2015 ); Massari ( 2010 ), SM as the environment for infectious contents like FN comprises of communication channels such as websites, mobile applications, and platforms that facititate relationship forming among users of contents with similar interest. Hence, the relevance of SM to various aspects of life is of high singficance to users, government policies, and the economy.

This is somewhat consistent with the argument of the Director-General of the World Health Organization (WHO) – Tedros Ghebreyesus – at a foreign policy and security expert submit held in Germany in February 2020 (Union, 2020 , May 19). Tedros argued that as the world continues to grapple with Covid-19 contagion, an ‘infodemic’ is emerging as FN continues to “spread faster and more” than Covid-19 (Africe, 2020 ). Given the speed of the spread of FN, infodemic can hinder the effectiveness of public health response while propagating confusion and distrust in the society.

As shown in Fig.  1 , the hosts interact with those who have similar interests in their SM groups or forums and thus recruit new believers to the environment (Haigh et al., 2018 ; Humprecht, 2019a ; Mettler & Winter, 2016 ; Roozenbeek & van der Linden, 2019 ; Rubin, 2019 ). These communities continue to grow as positive social networks expand. With the power of SM platforms, new groups are created that have a similar agenda, improving social learning and opportunities using SM platforms’ tools (Kwon et al., 2017 ). One of the purposes of these strategies and networks is to clamp down as quickly as possible on people perceived as outsiders that may uncover or expose their content and philosophies.

3 Research Method

3.1 research design and data collection.

This study carried out a longitudinal survey with online participants to test the relationships and associations in the proposed meta-framework. A cross-sectional online survey was conducted in 2019, survey was conducted using stratified sampling, with participants divided into groups based on their demographics, proficiency of using SM platforms, and interest in news and current affairs online. Table  2 shows participants’ profiles in terms of their gender, age, location, SM usage, and SM experience. The questionnaire was designed through the research gap and literature.

This study distributed the questionnaire to 2234 active engaging participants and received 546 surveys which included both partial and completed questionnaire, which accounts for a response rate of 24%, demonstrating that the response rate is consistent with previous studies (Arshad et al., 2014 ; Klashanov, 2018 ; Malik et al., 2020 ). This study sample size consists of participants from across the global, with North America accounting for 29% of the total survey which make up for the largest share in terms of participant size. Experience of using SM platforms show that 28% of the participants engage more than 5 times daily on the platforms while 22.7% accounting for participants with 5 to 6 years working the SM platforms.

3.2 Analytical Technique

According to Ragin ( 2013 ); Ragin and Pennings ( 2005 ), the fuzzy set theoretical approach can be used to evaluate theories, frameworks, and models with a deductive strategy driven by a positivist paradigm. Fuzzy set analysis is an emerging technique for management and social sciences, which has become more popular as the initial problems were overcome by introducing hybrid techniques of fuzzy set logic. This study adopts the relationship and association testing suggested by Ragin ( 2009 ) to test for Boolean expressions in the fuzzy set theoretical approach of the four intersections in Fig.  2 .

Integrated meta-framework

This study proposes an eight-step process flowchart consisting of four loop relationships (represented by the double line diamonds in Fig.  3 ) and three predictive relationships (represented by the single line diamonds) that shows the relationships used to discuss the outcomes of the analysis. The flowchart is described as follows:

Flow chart for the consistency analysis

A loop relationship for an expression that a solution pathway is reliable shows whether the consistency of the sufficiency analysis is greater than 0.7 of the solution pathways as defined in this paper for the consistency threshold analysis. Any relationship that falls below the set threshold is eliminated from further analysis testing, as this means that that relationship does not achieve acceptable reliability.

A loop relationship for an expression that a solution pathway is accepted shows whether the consistency of A1 is greater than 0.7. This statement suggests that any relationship that falls below the acceptable criteria in the solution pathway must be rejected.

A double line diamond relationship for a strongly supported expression shows whether the consistency of A2, A3, and A4 is less than or equal to 0.7. This statement suggests that any relationship that passes the acceptance criteria does not have significant contradictory proofs.

A single line diamond relationship for an expression not supported by itself (however, subsequent relationships can benefit) can be described by the consistency of A3, which is less than or equal to 0.7. Furthermore, A3 represents the type I consistency error, and it is usually below the acceptance threshold.

A loop relationship for an expression that a solution pathway is weakly supported shows whether the consistency of the sufficiency analysis that A1 is greater than A3 of the solution pathways, as defined for the consistency threshold analysis. Any relationship that falls below the set threshold is eliminated from further analysis, as the relationship does not achieve acceptable reliability.

A double line diamond relationship for a supported expression shows whether the consistency of A4 is less than or equal to 0.7. This statement suggests that any relationship that passes the acceptance criteria does not have a significant error during analysis and this supports classification.

A loop relationship for an expression that a solution pathway is not weakly supported shows whether the consistency of A2 is greater than 0.7. This statement suggests that any relationship that falls below the acceptable criteria in the solution pathway can be improved and there is weak support for classification.

A double line diamond relationship for a supported expression shows whether the consistency of A2 is greater than or equal to A4. This statement suggests that any relationship that passes the acceptance criteria and partially supports the conditions for A2 and A4 represents the type II consistency error; this is usually equal to or greater than the acceptance threshold.

4 Data Analysis and Results

According to Deutsch and Malmborg ( 1985 ), complementarity and equifinality, the two underlying features in the fuzzy set theoretic approach, display patterns of attributes and different results depending on the structure of the constructs. In addition, the attributes in the constructs are concerned with the present or absent conditions and associations formed during conceptualization, rather than isolating the attributes from the constructs. Furthermore, complementarity exists if there is proof that causal factors display a match in their attributes and the analysis shows a higher level in the results, while equifinality exists if at least two unidentical pathways known as causal factors show the same results (Herrera-Restrepo et al., 2016 ).

In Table  3 , the attributes of the constructs indicate the relationships that provide empirical evidence to reject or support the model. The results demonstrate that the relationships are mostly rejected. We find that a higher consistency level directly results in a higher reliability of the relationship. The three combinations of attributes in the sufficiency analysis show that the input efficiency either fails or passes the set consistency threshold requirement (consistency and coverage are 0.72 and 0.44, respectively).

In Table  4 , the relationships indicate support for the empirical findings. The results show that the attributes of the constructs have higher combined solution pathways than the attributes in Table  3 . The type II error (or false negative) is one form of contradiction ignored in Fig.  3 . These findings show the least likely attributes of the constructs, indicating the continuation of existing relationships as well as supporting the higher consistency level of the associations and stronger support for further relationships. Hence, this analysis can introduce additional causal conditions of similar attributes not yet shown in the current relationships by retracking to the relationship mapping data and finding common attributes in existing constructs. This may explain the undefined variance in the existing relationships.

Table  5 shows the combined solution pathways for consistency and coverage, indicating support for most of the attributes of the constructs. This indicates a type I error (or false positive) in the form of contradicting the variances in the relationships, while the higher consistency level of the associations supports the higher values that delimit the relationships. Therefore, unconfirmed attributes indicate a restriction of the current relationships.

In Table  6 , this combined solution pathway indicates that neither the predicted relationships nor the coverage by attributes’ definitions of the constructs are strongly supported in terms of societal acceptance and the challenges posed by FN on SM on society. Therefore, alternative variances, as understood by the society, are better-supporting conditions for the relationship’s definitions in A4. Five of the six pathways are equal to or greater than the defined threshold, indicating that the relationships between the constructs can benefit from trade-offs. Furthermore, there are similar results for the unique coverage, signaling a significantly high-efficiency input directly linked to the variance from the causal conditions.

To fully understand the A4 outcomes, it is important to discuss the outcomes from A1, A2, and A3 simultaneously. A1 and A2 are insufficient to support a high input efficiency, indicating that SM will fade-out without a correlation with FN. To have a high input efficiency, the combination of the two constructs is highly significant to the relationships. However, A3, which considers all the attributes in the societal acceptance constructs, rejects the associated attributes from A1, whereas it shows weak support for A2, which indicates that the conditions are peripheral or are unconcerned about the variance. This explains the weak support in the attributes of their relationships. The A4 outcome shows that this study considers the attributes of the relations between A1 and A2, as A3 can explain the outcomes of redefining and reducing the impact of both associations.

5 Discussion

The aim of this research was to carry out an investigation on the impact of FN on the society, the use of SM as a platform for cascading of information and news. Thus, this study further explore the conceptual model of disease triangle (Piccialli et al., 2021 ) which identify FN as infectious pathogen in Fig.  1 (SM platforms host and spread FN), without the societal acceptance, it is difficult to cascade information and news. Furthermore, FN as defined in this study holds three main features which are significant for the perceptions of the society: the contents of the news, the intentions of the news, and the verification of the news. Hence, the use of comparative technique (fsQCA analysis) to outline the findings as shown in this study auggesting that societal acceptance is important in understanding the impact of FN. To better understand FN, SM, and societal acceptance, this study developed a meta-framework and analyzed the relationships among the attributes of the three constructs within. An online survey with 356 participants was carried out with a stratified sample size to test the meta-framework, and the data collected from the survey process were further categorized as the relationships designed in the constructs. This study considered SM platforms and the activities stimuling cascading processes of FN, changing the societal acceptance through the lens of contents management.

In previous studies, SM platforms are increasingly changing business activities and strategies used in positioning new products and brands, also leading to mis-information in the society (Modgil et al., 2021 ; Parra et al., 2021 ; Piccialli et al., 2021 ), also analyzed the SM platforms as the environment for business and social transactions focusing on capturing the largest audiences for information cascading, this further the spread of FN through the use of cascading tools available on SM. According to (Dwivedi et al., 2018 ; Kim & Dennis, 2019 ; Kim et al., 2019 ), cascading of FN through the use of SM platforms is growing faster than anticipated. The results of this study identified focused areas that can reduce the spread of FN on SM.

The results gathered during data analysis of validated questionnaire demonstrated important contributions of this study to minimizing cascading of FN in the society. Thus, the evaluation of the three perspectives; FN, SM, and societal acceptance further enhanced into relationship mapping by considering the entities from each perspectives as shown in Fig.  2 . The results from Table  3 , suggest that the testing of the relationship A1: FN/USˑVA of FN perspective and the entities users and values of the societal perspective is rejected while the relationship A1: FN/USˑNW of FN perspective and the entities users and networks of the societal acceptance is supported. Furthermore, the outcomes in Table  3 concur with the disease triangle theory which discussed the pathology model for disease manifestation, stating that the three triangular elements for infectious pathogen must be present for disease to grow (Humprecht, 2019b ; Rubin, 2019 ; Sommariva et al., 2018 ). Hence, the relationship A1: FN/USˑVA of FN perspective and the entities users and values of the societal perspective lacks the environment (networks) for cascading of contents of FN.

Table  4 shows support for SM and societal acceptance perspectives relationship mapping, with constructs’ consistency and coverage meeting the set requirement in Fig.  3 . However, condition S1 and S2 for A2: SM/USˑVA and S1 for A2: SM/USˑNW were ignored from the result, suggesting that there are other sources of information such as true news, entertainment contents which users are engaging with on SM platforms. According to Kwon et al. ( 2017 ), SM platforms provide positive opportunities such as learning new skills, engaging with experienced individuals and mentors, and finding new friendship, directly impacting positively on the society.

The increase in the level of cascading of FN can be attributed to SM companies drive to upsurge the size of big data, leading to strategic end to end nodes multiplication (Haigh et al., 2018 ). This study demonstrates that the enabling environment for the spreading of FN is attributed to the structure and strategies of SM companies. As shown in Table  6 , when SM companies implement effective fact-checking tools on SM platforms, the traffic of FN is minimized and the impact on the society is reduced. The relevant role of SM companies is to ensure that verification and fact-checking are embedded into the process of retrieving news and information.

In summary, the findings of this study suggest that previous studies (Dwivedi et al., 2018 ; Kim et al., 2019 ; Malik et al., 2020 ; Modgil et al., 2021 ; Roozenbeek & van der Linden, 2019 ) demonstrated the gap for an investigation of the societal acceptance of contents available on SM. Our findings show that the societal acceptance of information and news is highly dependent on the verification and fact-checking features that are available on the SM platforms. Therefore, the research questions in this study outlined the need for fact-checking and verification of information and news most importantly FN on SM. The results of the complementarity assessments show that SM and societal acceptance did significantly influence cascading of contents towards users. Specifically, FN cascading spread faster than any other type of contents on SM as shown in Table  5 . With regards to societal acceptance, users distributions of FN contents unconsciously aid cascasding with the intention of spreading awareness about the situation surrounding FN events.

5.1 Theoretical Implications

This study builds on the theoretical knowledge in literature by making significant contribution to the understanding of the impact of FN and SM platforms on the society. According to studies (Abouzeid et al., 2021 ; Au et al., 2021 ; Dwivedi et al., 2018 ; Kim et al., 2019 ; Parra et al., 2021 ; Tran et al., 2021 ) with combined body of knowledge on misinformation, FN, SM, SM platforms, cascading of FN, and risks of misinformation, this study identifies three main themes in our contribution: FN, SM, and societal acceptance. Previous studies (Orso et al., 2020 ; Pennycook et al., 2020 ) have presented FN and SM concepts, however this study’s introduction of societal acceptance is a novel theoretical contribution. Furthermore, the lack of studies on the societal acceptance of cascading of FN have generated a theoretical gap in understanding FN, misinformation and SM. Therefore, the results in our paper filled the research gap by validating the proposed features of societal acceptance: users, networks, and values.

The findings of this study contribute to theory by using complementarity among FN, SM, and societal acceptance to explain their influence by evaluating all the attributes in the three constructs, building relationships, and presenting findings that identify the significance of each association to reduce the cascading of FN in society. Therefore, this research answers the call of studies (George et al., 2018 ; Miller & Tucker, 2013 ; Miranda et al., 2016 ) that have suggested further work on FN on SM. Further, this study explains the impact of FN on society by exploring the conditions in different scenarios and with different complementarity values. It also shows how SM (i.e., the environment) and users can strategically deploy all resources to tackle the cascading and spread of FN. Most importantly, fuzzy set theory provides a data analysis structure that shows complex causality, enabling this research to present empirical findings.

Theoretically speaking, the outcomes show the importance of fact-checking and managing cascading in reducing the spread of the contents of FN in the society. Also, the role of SM companies in continuance commitment to support the course of minizing the impact of FN. As of date, this is the first of study to develop a meta-framework to examine the impact of FN on the society distributed on the SM. This study argued that exploring fact-checking and managing cascading will provide a platform for SM companies to contributing in the challenging impact of FN on the society. This study finds that SM as a type of environment is equipped with the technological know-how to tackle the spread of FN. This is particularly so for large SM organizations such as Facebook whose main business is SM content. Therefore, investment in technological research and service innovation is becoming a priority. However, more investment is required for fact-checking and analyzing cascading news, meaning that SM organizations with technical research facilities are more likely to initiate rigorous fact-checking campaigns. Hence, profitability and market growth may be more important for implementing fact-checking and news-cascading technologies that benefit society.

5.2 Practical Implications

Based on the outcomes obtained from the complementarity of the fuzzy set, it is also important for the SM platform providers to continue to invest in the fact-checking and managing contents of FN that are influencing users perceptions. In addition, it is very important to manage the direct impact of FN contents on the society by increasing the amount of fact-checking and verification tools that are available on SM. For instance, vigorous campaigns on the important role of news and information verification across all SM platforms and ensuring that there is educating information about the impact of spreading FN on SM on the society at large. Also, SM organizations should implement safe technology such as real-time deletion of contents of FN to ensure a safer communication environment for the users. Furthermore, the distinguishing real news from fake news using aided technology will boost confidence in the society. The comprehensive theoretical review and in-depth empirical analysis of the complex casualty of FN on SM on society in this study allows SM organizations to consider their organizational strategies to reduce FN cascading and implement sustainable solutions. SM organizations should prioritize the allocation of resources toward measures that tackle the challenges FN poses to society as well as the cost, societal impact, and misinformation linked to regulations to halt the spread of FN.

5.3 Implications for Society

The in-depth empirical analysis conducted concerning the FN on SM and the societal impact, the study provides a platform to the SM users on how far the facts published on SM can be trusted and how to filter the FN from TN on SM. SM organizations such as Facebook and Twitter have invested in large to tackle the publishing of FN on social media while yet the FN has taken on SM drastically during certain urgent situations.

Following the countless challenges that arose around the world due to the FN published on SM and the societal impact, the SM organizations have taken larger steps in minimizing the FN before being published and open to the public. The flowchart for the consistency analysis can be used by SM organizations in analyzing the published news on SM to distinguish FN from TN. Thus, the negative impact caused by FN to users and their lives can be minimized. Despite the fact that steps been taken by the SM organizations, it is also users’ responsibility to filter TN from FN even if they are being posted on verified accounts, by fact-checking or using appropriate verification (Nagi, 2020 ).

6 Conclusions

The results from this study demonstrate that it is important for SM platform providers continue in their efforts to understand the risks of cascading of FN and the influence on the society at large. Hence, the implementation of fact-checking tools is significant in reducing the spread of FN, building of trust and confident in the society. SM platform providers should ensure that there is continuous monitoring of online activities triggered by spread of FN and also ensures periodic upgrade of fact-checking technologies to tackle new tricks and strategies used in cascading FN in the society (Modgil et al., 2021 ; Parra et al., 2021 ). Furthermore, fact-checking information and public awareness on how to verify news can be added to campaigns to support the affected societies in combating the impact of FN. The findings in our study demonstrate that societal acceptance is a powerful tool that can persuade the society to focus on achieving common goal. The role of the society is to adopt the strength in societal acceptance to drive positive cultural change that welcome fact-checking and verification of any form of news.

6.1 Limitations and Future Research Directions

This study, like other studies, has limitations that suggest future research directions. This study analyzed how three constructs, FN, SM, and societal acceptance, impact on society. Other constructs were not included in this study such as SM firms’ power, political strategies, and societal perceptions. In addition, our data collection focused on people who engage most frequently with SM; experts and SM analysts may be relevant for future research to examine. Given that previous researchers focus on cascading FN and fact-checking news content to distinguish TN from FN, the influence of fact-checking and analyzing FN cascading could be tested future research with new datasets. In this vein, this study did not consider the financial impact of FN on SM on society, which is another interesting area for future research.

This cross-sectional research aimed to provide an in-depth understanding of the relationships of the three studied topics by analyzing data from many demographics rather than from one location. Therefore, the findings of this study support generalization to many locations. However, since some studies consider the results from a single location, future research could compare the complementarity, consistency, and coverage of a single location with many locations, which would enrich the findings of this study.

Abouzeid, A., Granmo, O. C., Webersik, C., & Goodwin, M. (2021). Learning automata-based misinformation mitigation via Hawkes processes. Information Systems Frontiers, 23 (5), 1169–1188. https://doi.org/10.1007/s10796-020-10102-8 .

Article   Google Scholar  

Africe, W. R. O. (2020). f. Technical Guidance on contact tracingor COVID-19 in the World Health Organization (WHO) African region .  https://www.afro.who.int/publications/technical-guidance-contact-tracing-covid-19-world-health-organization-who-african . Accessed 19 May 2020.

Aggarwal, R., Gopal, R., Sankaranarayanan, R., & Singh, P. V. (2012). Blog, blogger, and the firm: can negative employee posts lead to positive outcomes? Information Systems Research, 23 (2), 306–322. https://doi.org/10.1287/isre.1110.0360 .

Aggarwal, R., & Singh, H. (2013). Differential influence of blogs across different stages of decision making: the case of venture capitalists.(Report). Mis Quarterly, 37 (4), 1093. https://doi.org/10.25300/MISQ/2013/37.4.05 .

Arshad, M., Islam, S., & Khaliq, A. (2014). Fuzzy logic approach in power transformers management and decision making. IEEE Transactions on Dielectrics and Electrical Insulation, 21 (5), 2343–2354. https://doi.org/10.1109/TDEI.2014.003859 .

Au, C. H., Ho, K. K. W., & Chiu, D. K. W. (2021). The role of online misinformation and fake news in ideological polarization: barriers, catalysts, and implications. Information Systems Frontiers . https://doi.org/10.1007/s10796-021-10133-9 .

Barrett, M., Oborn, E., & Orlikowski, W. (2016). Creating value in online communities: the sociomaterial configuring of strategy, platform, and stakeholder engagement. Information Systems Research, 27 (4), 704–723. https://doi.org/10.1287/isre.2016.0648 .

Baur, A. (2017). Harnessing the social web to enhance insights into people’s opinions in business, government and public administration. Information Systems Frontiers, 19 (2), 231–251. https://doi.org/10.1007/s10796-016-9681-7 .

Berkowitz, D., & Schwartz, D. A. (2016). Miley, CNN and The Onion. Journalism Practice, 10 (1), 1–17. https://doi.org/10.1080/17512786.2015.1006933 .

Brennen, B. (2017). Making sense of lies, deceptive propaganda, and fake news. Journal of Media Ethics, 32 (3), 179–181. https://doi.org/10.1080/23736992.2017.1331023 .

Brummette, J., Distaso, M., Vafeiadis, M., & Messner, M. (2018). Read all about it: the politicization of “Fake News” on Twitter. Journalism & Mass Communication Quarterly, 95 (2), 497–517. https://doi.org/10.1177/1077699018769906 .

Burkhardt, J. M. (2017). History of fake news. Library Technology Reports, 53 (8), 5–9.

Google Scholar  

Cao, X., Guo, X., Liu, H., & Gu, J. (2015). The role of social media in supporting knowledge integration: A social capital analysis. Information Systems Frontiers, 17 (2), 351–362. https://doi.org/10.1007/s10796-013-9473-2 .

Centeno, R., Hermoso, R., & Fasli, M. (2015). On the inaccuracy of numerical ratings: dealing with biased opinions in social networks. Information Systems Frontiers, 17 (4), 809–825. https://doi.org/10.1007/s10796-014-9526-1 .

Chang, I. C., Liu, C. C., & Chen, K. (2014). The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal, 24 (4), 323–346. https://doi.org/10.1111/isj.12030 .

Chang, W. L., Diaz, A., & Hung, P. (2015). Estimating trust value: A social network perspective. Information Systems Frontiers, 17 (6), 1381–1400. https://doi.org/10.1007/s10796-014-9519-0 .

Chen, H., De, P., & Hu, Y. J. (2015). IT-enabled broadcasting in social media: an empirical study of artists’ activities and music sales. Information Systems Research, 26 (3), 513–531. https://doi.org/10.1287/isre.2015.0582 .

Chen, R., & Sharma, S. K. (2015). Learning and self-disclosure behavior on social networking sites: the case of Facebook users. European Journal of Information Systems, 24 (1), 93–106. https://doi.org/10.1057/ejis.2013.31 .

Chen, X. (2018). Calling out fake news on social media: a comparison of literature in librarianship and journalism. Internet Reference Services Quarterly, 23 (1–2), 1–13. https://doi.org/10.1080/10875301.2018.1518284 .

Copeland, D. A. (2007). A series of fortunate events: why people believed Richard Adams Locke’s “Moon Hoax.” Journalism History, 33 (3), 140–150.

Deutsch, S. J., & Malmborg, C. J. (1985). Evaluating organizational performance-measures using fuzzy subsets. European Journal of Operational Research, 22 (2), 234–242. https://doi.org/10.1016/0377-2217(85)90231-0 .

Dwivedi, Y. K., Kelly, G., Janssen, M., Rana, N. P., Slade, E. L., & Clement, M. (2018). Social media: the good, the bad, and the ugly. Information Systems Frontiers, 20 (3), 419–423. https://doi.org/10.1007/s10796-018-9848-5 .

Egelhofer, J. L., & Lecheler, S. (2019). Fake news as a two-dimensional phenomenon: a framework and research agenda. Annals of the International Communication Association, 43 (2), 97–116. https://doi.org/10.1080/23808985.2019.1602782 .

Fang, X., Hu, P. J. H., Li, Z., & Tsai, W. (2013). Predicting adoption probabilities in social networks. Information Systems Research, 24 (1), 128–145. https://doi.org/10.1287/isre.1120.0461 .

Garg, R., Smith, M. D., & Telang, R. (2011). Measuring information diffusion in an online community. Journal of Management Information Systems, 28 (2), 11–38.

George, J. F., Gupta, M., Giordano, G., Mills, A. M., Tennant, V. M., & Lewis, C. C. (2018). The effects of communication media and culture on deception detection accuracy. MIS Quarterly: Management Information Systems, 42 (2), 551–575. https://doi.org/10.25300/MISQ/2018/13215 .

Gerlach, J., Widjaja, T., & Buxmann, P. (2015). Handle with care: How online social network providers’ privacy policies impact users’ information sharing behavior. Journal of Strategic Information Systems, 24 (1), 33–43. https://doi.org/10.1016/j.jsis.2014.09.001 .

Gomez-Miranda, M. E., Perez-Lopez, M. C., Argente-Linares, E., & Rodriguez-Ariza, L. (2015). The impact of organizational culture on competitiveness, effectiveness and efficiency in Spanish-Moroccan international joint ventures. Personnel Review, 44 (3), 364–387. https://doi.org/10.1108/Pr-07-2013-0119 .

Gray, P., Parise, S., & Iyer, B. (2011). Innovation impacts of using social bookmarking systems. Mis Quarterly, 35 (3), 629–643. https://doi.org/10.2307/23042800 .

Haigh, M., Haigh, T., & Kozak, N. I. (2018). Stopping fake news. Journalism Studies, 19 (14), 2062–2087. https://doi.org/10.1080/1461670X.2017.1316681 .

Hamamreh, R. A., & Awad, S. (2017). 14-16 Dec. 2017). Tag ranking multi-agent semantic social networks. 2017 International Conference on Computational Science and Computational Intelligence (CSCI)

Han, J., Lee, S. H., & Kim, J. K. (2017). A process integrated engineering knowledge acquisition and management model for a project based manufacturing (Vol 18, pg 175, 2017). International Journal of Precision Engineering and Manufacturing , 18 (3), 467-467. https://doi.org/10.1007/s12541-017-0056-x

Herrera-Restrepo, O., Triantis, K., Trainor, J., Murray-Tuite, P., & Edara, P. (2016). A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach. Omega-International Journal of Management Science, 60, 45–59. https://doi.org/10.1016/j.omega.2015.04.019 .

Humprecht, E. (2019). How do they debunk “fake news”? A cross-national comparison of transparency in fact checks. Digital Journalism . https://doi.org/10.1080/21670811.2019.1691031 .

Humprecht, E. (2019). Where ‘fake news’ flourishes: a comparison across four Western democracies. Information Communication and Society, 22 (13), 1973–1988. https://doi.org/10.1080/1369118X.2018.1474241 .

Hwang, Y. C., Yuan, S. T., & Weng, J. H. (2011). A study of the impacts of positive/negative feedback on collective wisdom—case study on social bookmarking sites. Information Systems Frontiers, 13 (2), 265–279. https://doi.org/10.1007/s10796-009-9186-8 .

Kapoor, K., Tamilmani, K., Rana, N., Patil, P., Dwivedi, Y., & Nerur, S. (2018). Advances in social media research: past, present and future. Information Systems Frontiers, 20 (3), 531–558. https://doi.org/10.1007/s10796-017-9810-y .

Kim, A., & Dennis, A. R. (2019). Says who? The effects of presentation format and source rating on fake news in social media. MIS Quarterly: Management Information Systems, 43 (3), 1025–1039. https://doi.org/10.25300/MISQ/2019/15188 .

Kim, A., Moravec, P. L., & Dennis, A. R. (2019). Combating fake news on social media with source ratings: the effects of user and expert reputation ratings. Journal of Management Information Systems, 36 (3), 931–968.

Kim, E. H., & Lyon, T. (2014). Greenwash vs. Brownwash: Exaggeration and undue modesty in corporate sustainability disclosure. Organization Science, 26 (3), 705–723. https://doi.org/10.1287/orsc.2014.0949 .

Klashanov, F. (2018). Fuzzy logic in construction management. MATEC Web of Conferences , 170 . https://doi.org/10.1051/matecconf/201817001111

Knight, E., & Tsoukas, H. (2019). When Fiction Trumps Truth: What ‘post-truth’ and ‘alternative facts’ mean for management studies. Organization Studies, 40 (2), 183–197. https://doi.org/10.1177/0170840618814557 .

Kuem, J., Ray, S., Siponen, M., & Kim, S. S. (2017). What leads to prosocial behaviors on social networking services: a tripartite model. Journal of Management Information Systems, 34 (1), 40–70. https://doi.org/10.1080/07421222.2017.1296744 .

Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2018). Detecting review manipulation on online platforms with hierarchical supervised learning. Journal of Management Information Systems, 35 (1), 350–380.

Kwon, H. E., Oh, W., & Kim, T. (2017). Platform structures, homing preferences, and homophilous propensities in online social networks. Journal of Management Information Systems, 34 (3), 768–802.

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F. … Zittrain, J. L. (2018). The science of fake news: Addressing fake news requires a multidisciplinary effort. Science , 359 (6380), 1094–1096. https://doi.org/10.1126/science.aao2998

Leong, C., Pan, S., Ractham, P., & Kaewkitipong, L. (2015). ICT-enabled community empowerment in crisis response: social media in Thailand flooding 2011. Journal of the Association for Information Systems, 16 (3), 174–212. https://doi.org/10.17705/1jais.00390 .

Lukyanenko, R., Parsons, J., & Wiersma, Y. F. (2014). The IQ of the crowd: understanding and improving information quality in structured user-generated content. Information Systems Research, 25 (4), 669–689. https://doi.org/10.1287/isre.2014.0537 .

Lundmark, L., Oh, C., & Verhaal, J. (2017). A little Birdie told me: Social media, organizational legitimacy, and underpricing in initial public offerings. Information Systems Frontiers, 19 (6), 1407–1422. https://doi.org/10.1007/s10796-016-9654-x .

Lyon, T. P., & Montgomery, A. W. (2015). The means and end of greenwash. Organization & Environment, 28 (2), 223–249. https://doi.org/10.1177/1086026615575332 .

Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015). Giving too much social support: social overload on social networking sites. European Journal of Information Systems, 24 (5), 447–464. https://doi.org/10.1057/ejis.2014.3 .

Malik, A., Froese, F. J., & Sharma, P. (2020). Role of HRM in knowledge integration: Towards a conceptual framework. Journal of Business Research, 109, 524–535. https://doi.org/10.1016/j.jbusres.2019.01.029 .

Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. The Review of Economic Studies, 60 (3), 531–542. https://doi.org/10.2307/2298123 .

Massari, L. (2010). Analysis of MySpace user profiles. Information Systems Frontiers, 12 (4), 361–367. https://doi.org/10.1007/s10796-009-9206-8 .

Matook, S., Cummings, J., & Bala, H. (2015). Are you feeling lonely? The impact of relationship characteristics and online social network features on loneliness. Journal of Management Information Systems, 31 (4), 278–310.

Mettler, T., & Winter, R. (2016). Are business users social? A design experiment exploring information sharing in enterprise social systems. Journal of Information Technology, 31 (2), 101–114. https://doi.org/10.1057/jit.2015.28 .

Miller, A. R., & Tucker, C. (2013). Active social media management: the case of health care. Information Systems Research, 24 (1), 52–70. https://doi.org/10.1287/isre.1120.0466 .

Miranda, S. M., Kim, I., & Summers, J. D. (2015). Jamming with social media: How cognitive structuring of organizing vision facets affects it innovation diffusion. Mis Quarterly, 39 (3), 591. https://doi.org/10.25300/MISQ/2015/39.3.04 .

Miranda, S. M., Young, A., & Yetgin, E. (2016). Are social media emancipatory or hegemonic? Societal effects of mass media digitization in the case of the sopa discourse. Mis Quarterly, 40 (2), 303. https://doi.org/10.25300/MISQ/2016/40.2.02 .

Modgil, S., Singh, R. K., Gupta, S., & Dennehy, D. (2021). A confirmation bias view on social media induced polarisation during Covid-19.  Information Systems Frontiers . https://doi.org/10.1007/s10796-021-10222-9 .

Nagi, K. (2020). From bits and bytes to big data-An historical overview . Available at SSRN 3622921.

Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: an author co-citation analysis. Strategic Management Journal, 29 (3), 319–336. https://doi.org/10.1002/smj.659 .

Oestreicher-Singer, G., & Zalmanson, L. (2013). Content or community? A digital business strategy for content providers in the social age.(Special Issue: Digital Business Strategy)(Report). Mis Quarterly, 37 (2), 591. https://doi.org/10.25300/MISQ/2013/37.2.12 .

Orso, D., Federici, N., Copetti, R., Vetrugno, L., & Bove, T. (2020). Infodemic and the spread of fake news in the COVID-19-era.  European Journal of Emergency Medicine

Pan, Z., Lu, Y., Wang, B., & Chau, P. Y. K. (2017). Who do you think you are? Common and differential effects of social self-identity on social media usage. Journal of Management Information Systems, 34 (1), 71–101.

Parra, C. M., Gupta, M., & Dennehy, D. (2021). Likelihood of questioning ai-based recommendations due to perceived racial/gender bias. IEEE Transactions on Technology and Society

Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological Science, 31 (7), 770–780.

Piccialli, F., di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers, 23 (6), 1467–1497. https://doi.org/10.1007/s10796-021-10131-x .

Pierri, F., Artoni, A., & Ceri, S. (2020). Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections. Plos One, 15 (1), e0227821. https://doi.org/10.1371/journal.pone.0227821 .

Posetti, J., & Matthews, A. (2018). A short guide to the history of ‘fake news’ and disinformation.  International Center For Journalists , 2018–2007

Preti, A., & Miotto, P. (2011). Self-deception, social desirability, and psychopathology. Behavioral and Brain Sciences, 34 (1), 37–37. https://doi.org/10.1017/S0140525X10002487 .

Prosser, C., Fieldhouse, E., Green, J., Mellon, J., & Evans, G. (2020). Tremors but no Youthquake: Measuring changes in the age and turnout gradients at the 2015 and 2017 British general elections. Electoral Studies, 64 . https://doi.org/10.1016/j.electstud.2020.102129 .

Ragin, C. (2013). New directions in the logic of social inquiry. Political Research Quarterly, 66 (1), 171–174.

Ragin, C. C. (2009). Qualitative comparative analysis using fuzzy sets (fsQCA). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques , 51 , 87-121

Ragin, C. C., & Pennings, P. (2005). Fuzzy sets and social research. Sociological Methods & Research, 33 (4), 423–430. https://doi.org/10.1177/0049124105274499 .

Roozenbeek, J., & van der Linden, S. (2019). The fake news game: actively inoculating against the risk of misinformation. Journal of Risk Research, 22 (5), 570–580. https://doi.org/10.1080/13669877.2018.1443491 .

Rubin, V. L. (2019). Disinformation and misinformation triangle. Journal of Documentation, 75 (5), 1013–1034. https://doi.org/10.1108/JD-12-2018-0209 .

Scholthof, K. B. G. (2007). The disease triangle: pathogens, the environment and society. Nature Reviews Microbiology, 5 (2), 152–156.

Sommariva, S., Vamos, C., Mantzarlis, A., Đào, L. U. L., & Martinez Tyson, D. (2018). Spreading the (fake) news: exploring health messages on social media and the implications for health professionals using a case study. American Journal of Health Education, 49 (4), 246–255. https://doi.org/10.1080/19325037.2018.1473178 .

Tandoc, E. C., Jenkins, J., & Craft, S. (2019). Fake news as a critical incident in journalism. Journalism Practice, 13 (6), 673–689. https://doi.org/10.1080/17512786.2018.1562958 .

Tandoc, E. C., Lim, Z. W., & Ling, R. (2018). Defining “fake news.” Digital Journalism, 6 (2), 137–153. https://doi.org/10.1080/21670811.2017.1360143 .

Tran, T., Valecha, R., Rad, P., & Rao, H. R. (2021). An investigation of misinformation harms related to social media during two humanitarian crises. Information Systems Frontiers, 23 (4), 931–939. https://doi.org/10.1007/s10796-020-10088-3 .

Union, U. (2020). UN tackles ‘infodemic’ of misinformation and cybercrime in COVID-19 crisis .  https://www.un.org/en/un-coronavirus-communications-team/un-tackling-%E2%80%98infodemic%E2%80%99-misinformation-and-cybercrime-covid-19 . Accessed 19 May 2020.

Venkatraman, S., Cheung, M. K., Lee, C., Davis, Z. W. Y. D., & Venkatesh, V. (2018). The “Darth” side of technology use: an inductively derived typology of cyberdeviance. Journal of Management Information Systems, 35 (4), 1060–1091. https://doi.org/10.1080/07421222.2018.1523531 .

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359 (6380), 1146–1151. https://doi.org/10.1126/science.aap9559 .

Wang, Y., Li, Y., & Luo, J. (2016). Deciphering the 2016 US Presidential campaign in the Twitter sphere: A comparison of the Trumpists and Clintonists . Tenth International AAAI Conference on Web and Social Media

Download references

Author information

Authors and affiliations.

Newcastle Business School, Northumbria University, Newcastle Upon Tyne, UK

Femi Olan & Emmanuel Ogiemwonyi Arakpogun

School of Business and Economics, Loughborough University, Loughborough, UK

Uchitha Jayawickrama

NIHR Newcastle IVD Co-operative Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK

Jana Suklan

Plymouth Business School, University of Plymouth, Plymouth, UK

Shaofeng Liu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Femi Olan .

Ethics declarations

Conflict of interest.

There is no conflict of interest and no funding was received for conducting this study. Also, All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this study.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Olan, F., Jayawickrama, U., Arakpogun, E.O. et al. Fake news on Social Media: the Impact on Society. Inf Syst Front (2022). https://doi.org/10.1007/s10796-022-10242-z

Download citation

Accepted : 05 January 2022

Published : 19 January 2022

DOI : https://doi.org/10.1007/s10796-022-10242-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Misinformation
• Societal acceptance
• Social media
• Societal values
• Find a journal
• Publish with us
• Track your research

December 1, 2020

15 min read

Information Overload Helps Fake News Spread, and Social Media Knows It

Understanding how algorithm manipulators exploit our cognitive vulnerabilities empowers us to fight back

By Filippo Menczer & Thomas Hills

Cristina Spanò

C onsider Andy, who is worried about contracting COVID in 2020. Unable to read all the articles he sees on it, he relies on trusted friends for tips. When one opines on Facebook that pandemic fears are overblown, Andy dismisses the idea at first. But then the hotel where he works closes its doors, and with his job at risk, Andy starts wondering how serious the threat from the virus really is. No one he knows has died, after all. A colleague posts an article about the COVID “scare” having been created by Big Pharma in collusion with corrupt politicians, which jibes with Andy’s distrust of government. His Web search quickly takes him to articles claiming that COVID is no worse than the flu. Andy joins an online group of people who have been or fear being laid off and soon finds himself asking, like many of them, “What pandemic?” When he learns that several of his new friends are planning to attend a rally demanding an end to lockdowns, he decides to join them. Almost no one at the massive protest, including him, wears a mask. When his sister asks about the rally, Andy shares the conviction that has now become part of his identity: COVID is a hoax.

This example illustrates a minefield of cognitive biases. We prefer information from people we trust, our in-group. We pay attention to and are more likely to share information about risks—for Andy, the risk of losing his job. We search for and remember things that fit well with what we already know and understand. These biases are products of our evolutionary past, and for tens of thousands of years, they served us well. People who behaved in accordance with them—for example, by staying away from the overgrown pond bank where someone said there was a viper—were more likely to survive than those who did not.

Modern technologies are amplifying these biases in harmful ways, however. Search engines direct Andy to sites that inflame his suspicions, and social media connects him with like-minded people, feeding his fears. Making matters worse, bots—automated social media accounts that impersonate humans—enable misguided or malevolent actors to take advantage of his vulnerabilities.

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

Compounding the problem is the proliferation of online information. Viewing and producing blogs, videos, tweets and other units of information called memes have become so cheap and easy that the information marketplace is inundated. Unable to process all this material, we let our cognitive biases decide what we should pay attention to. These mental shortcuts influence which information we search for, comprehend, remember and repeat to a harmful extent.

The need to understand these cognitive vulnerabilities and how algorithms use or manipulate them has become urgent. At the University of Warwick in England and at Indiana University Bloomington’s Observatory on Social Media (OSoMe, pronounced “awesome”), our teams are using cognitive experiments, simulations, data mining and artificial intelligence to comprehend the cognitive vulnerabilities of social media users. Insights from psychological studies on the evolution of information conducted at Warwick inform the computer models developed at Indiana, and vice versa. We are also developing analytical and machine-learning aids to fight social media manipulation. Some of these tools are already being used by journalists, civil-society organizations and individuals to detect inauthentic actors, map the spread of false narratives and foster news literacy.

Information Overload

The glut of information has generated intense competition for people’s attention. As Nobel Prize–winning economist and psychologist Herbert A. Simon noted, “What information consumes is rather obvious: it consumes the attention of its recipients.” One of the first consequences of the so-called attention economy is the loss of high-quality information. The OSoMe team demonstrated this result with a set of simple simulations. It represented users of social media such as Andy, called agents, as nodes in a network of online acquaintances. At each time step in the simulation, agents may either create a meme or reshare one that they see in a news feed. To mimic limited attention, agents are allowed to view only a certain number of items near the top of their news feeds.

Running this simulation over many time steps, Lilian Weng, now at OpenAI, and researchers at OSoMe found that as agents’ attention became increasingly limited, the propagation of memes came to reflect the power-law distribution of actual social media: the probability that a meme would be shared a given number of times was roughly an inverse power of that number. For example, the likelihood of a meme being shared three times was approximately nine times less than that of its being shared once.

This winner-take-all popularity pattern of memes, in which most are barely noticed while a few spread widely, could not be explained by some of them being more catchy or somehow more valuable: the memes in this simulated world had no intrinsic quality. Virality resulted purely from the statistical consequences of information proliferation in a social network of agents with limited attention. Even when agents preferentially shared memes of higher quality, researcher Xiaoyan Qiu, then at OSoMe, observed little improvement in the overall quality of those shared the most. Our models revealed that even when we want to see and share high-quality information, our inability to view everything in our news feeds inevitably leads us to share things that are partly or completely untrue.

Source: “Limited Individual Attention and Online Virality of Low-Quality Information,” by Xiaoyan Qiu et al., in Nature Human Behaviour , Vol. 1, June 2017

Cognitive biases greatly worsen the problem. In a set of groundbreaking studies in 1932, psychologist Frederic Bartlett told volunteers a Native American legend about a young man who hears war cries and, pursuing them, enters a dreamlike battle that eventually leads to his real death. Bartlett asked the volunteers, who were non-Native, to recall the rather confusing story at increasing intervals, from minutes to years later. He found that as time passed, the rememberers tended to distort the tale’s culturally unfamiliar parts such that they were either lost to memory or transformed into more familiar things. We now know that our minds do this all the time: they adjust our understanding of new information so that it fits in with what we already know. One consequence of this so-called confirmation bias is that people often seek out, recall and understand information that best confirms what they already believe.

This tendency is extremely difficult to correct. Experiments consistently show that even when people encounter balanced information containing views from differing perspectives, they tend to find supporting evidence for what they already believe. And when people with divergent beliefs about emotionally charged issues such as climate change are shown the same information on these topics, they become even more committed to their original positions.

Making matters worse, search engines and social media platforms provide personalized recommendations based on the vast amounts of data they have about users’ past preferences. They prioritize information in our feeds that we are most likely to agree with—no matter how fringe—and shield us from information that might change our minds. This makes us easy targets for polarization. Nir Grinberg and his co-workers at Northeastern University showed in 2019 that conservatives in the U.S. are more receptive to misinformation. But our own analysis of consumption of low-quality information on Twitter shows that the vulnerability applies to both sides of the political spectrum, and no one can fully avoid it. Even our ability to detect online manipulation is affected by our political bias, though not symmetrically : Republican users are more likely to mistake bots promoting conservative ideas for humans, whereas Democrats are more likely to mistake conservative human users for bots.

Social Herding

In New York City in August 2019, people began running away from what sounded like gunshots. Others followed, some shouting, “Shooter!” Only later did they learn that the blasts came from a backfiring motorcycle. In such a situation, it may pay to run first and ask questions later. In the absence of clear signals, our brains use information about the crowd to infer appropriate actions, similar to the behavior of schooling fish and flocking birds.

Such social conformity is pervasive. In a fascinating 2006 study involving 14,000 Web-based volunteers, Matthew Salganik, then at Columbia University, and his colleagues found that when people can see what music others are downloading, they end up downloading similar songs. Moreover, when people were isolated into “social” groups, in which they could see the preferences of others in their circle but had no information about outsiders, the choices of individual groups rapidly diverged. But the preferences of “nonsocial” groups, where no one knew about others’ choices, stayed relatively stable. In other words, social groups create a pressure toward conformity so powerful that it can overcome individual preferences, and by amplifying random early differences, it can cause segregated groups to diverge to extremes.

Credit: Filippo Menczer

Social media follows a similar dynamic. We confuse popularity with quality and end up copying the behavior we observe. Experiments on Twitter by Bjarke Mønsted, then at the Technical University of Denmark, and his colleagues indicate that information is transmitted via “complex contagion”: when we are repeatedly exposed to an idea, typically from many sources, we are more likely to adopt and reshare it. This social bias is further amplified by what psychologists call the “mere exposure effect”: when people are repeatedly exposed to the same stimuli, such as certain faces, they grow to like those stimuli more than those they have encountered less often.

Such biases translate into an irresistible urge to pay attention to information that is going viral—if everybody else is talking about it, it must be important. In addition to showing us items that conform with our views, social media platforms such as Facebook, Twitter, YouTube and Instagram place popular content at the top of our screens and show us how many people have liked and shared something. Few people realize that these cues do not provide independent assessments of quality.

In fact, programmers who design the algorithms for ranking memes on social media assume that the “wisdom of crowds” will quickly identify high-quality items; they use popularity as a proxy for quality. Our analysis of vast amounts of anonymous data about clicks shows that all platforms—social media, search engines and news sites—preferentially serve up information from a narrow subset of popular sources.

To understand why, we modeled how they combine signals for quality and popularity in their rankings. In this model, agents with limited attention—those who see only a given number of items at the top of their news feeds—are also more likely to click on memes ranked higher by the platform. Each item has intrinsic quality, as well as a level of popularity determined by how many times it has been clicked on. Another variable tracks the extent to which the ranking relies on popularity rather than quality. Simulations of this model reveal that such algorithmic bias typically suppresses the quality of memes even in the absence of human bias. Even when we want to share the best information, the algorithms end up misleading us.

Echo Chambers

Most of us do not believe we follow the herd. But our confirmation bias leads us to follow others who are like us, a dynamic that is sometimes referred to as homophily—a tendency for like-minded people to connect with one another. Social media amplifies homophily by allowing users to alter their social network structures through following, unfriending, and so on. The result is that people become segregated into large, dense and increasingly misinformed communities commonly described as echo chambers.

At OSoMe, we explored the emergence of online echo chambers through another simulation, EchoDemo . In this model, each agent has a political opinion represented by a number ranging from −1 (say, liberal) to +1 (conservative). These inclinations are reflected in agents’ posts. Agents are also influenced by the opinions they see in their news feeds, and they can unfollow users with dissimilar opinions. Starting with random initial networks and opinions, we found that the combination of social influence and unfollowing greatly accelerates the formation of polarized and segregated communities.

Credit: Jen Christiansen; Source: Dimitar Nikolov and Filippo Menczer ( data )

Indeed, the political echo chambers on Twitter are so extreme that individual users’ political leanings can be predicted with high accuracy : you have the same opinions as the majority of your connections. This chambered structure efficiently spreads information within a community while insulating that community from other groups. In 2014 our research group was targeted by a disinformation campaign claiming that we were part of a politically motivated effort to suppress free speech. This false charge spread virally mostly in the conservative echo chamber, whereas debunking articles by fact-checkers were found mainly in the liberal community. Sadly, such segregation of fake news items from their fact-check reports is the norm.

Social media can also increase negativity. In a 2018 laboratory study, Robert Jagiello, now at the University of Oxford, and one of us (Hills) found that socially shared information not only bolsters biases but also becomes more resilient to correction. We investigated how information is passed from person to person in a so-called social diffusion chain. In the experiment, the first person in the chain read a set of articles about either nuclear power or food additives. The articles were designed to be balanced, containing as much positive information (for example, about less carbon pollution or longer-lasting food) as negative information (such as risk of meltdown or possible harm to health).

The first person in the social diffusion chain told the next person about the articles, the second told the third, and so on. We observed an overall increase in the amount of negative information as it passed along the chain—known as the social amplification of risk. Moreover, work by Danielle J. Navarro and her colleagues at the University of New South Wales in Australia found that information in social diffusion chains is most susceptible to distortion by individuals with the most extreme biases.

Even worse, social diffusion also makes negative information more “sticky.” When Jagiello and Hills subsequently exposed people in the social diffusion chains to the original, balanced information—that is, the news that the first person in the chain had seen—the balanced information did little to reduce individuals’ negative attitudes. The information that had passed through people not only had become more negative but also was more resistant to updating.

A 2015 study by Emilio Ferrara and Zeyao Yang, then both OSoMe researchers, analyzed empirical data about such “emotional contagion” on Twitter and found that people overexposed to negative content tend to share negative posts, whereas those overexposed to positive content tend to share more positive posts. Because negative content spreads faster than positive content, it is easy to manipulate emotions by creating narratives that trigger negative responses such as fear and anxiety. Ferrara, now at the University of Southern California, and his colleagues at the Bruno Kessler Foundation in Italy have shown that during Spain’s 2017 referendum on Catalan independence, social bots were leveraged to retweet violent and inflammatory narratives , increasing their exposure and exacerbating social conflict.

Rise of the Bots

Information quality is further impaired by social bots, which can exploit all our cognitive loopholes. Bots are easy to create. Social media platforms provide so-called application programming interfaces that make it fairly trivial for a single actor to set up and control thousands of bots. But amplifying a message, even with just a few early upvotes by bots on social media platforms such as Reddit, can have a huge impact on the subsequent popularity of a post.

At OSoMe, we have developed machine-learning algorithms to detect social bots. One of these, Botometer , is a public tool that extracts 1,200 features from a given Twitter account to characterize its profile, friends, social network structure, temporal activity patterns, language and other features. The program compares these characteristics with those of tens of thousands of previously identified bots to give the Twitter account a score for its likely use of automation.

In 2017 we estimated that up to 15 percent of active Twitter accounts were bots—and that they had played a key role in the spread of misinformation during the 2016 U.S. election period. Within seconds of a fake news article being posted—such as one claiming the Clinton campaign was involved in occult rituals—it would be tweeted by many bots, and humans, beguiled by the apparent popularity of the content, would retweet it.

Bots also influence us by pretending to represent people from our in-group. A bot only has to follow, like and retweet someone in an online community to quickly infiltrate it. Xiaodan Lou of Beijing Normal University, working with OSoMe, developed another model in which some of the agents are bots that infiltrate a social network and share deceptively engaging low-quality content—think of clickbait. One parameter in the model describes the probability that an authentic agent will follow bots—which, for the purposes of this model, we define as agents that generate memes of zero quality and retweet only one another. Our simulations show that these bots can effectively suppress the entire ecosystem’s information quality by infiltrating only a small fraction of the network. Bots can also accelerate the formation of echo chambers by suggesting other inauthentic accounts to be followed, a technique known as creating “follow trains.”

Some manipulators play both sides of a divide through separate fake news sites and bots, driving political polarization or monetization by ads. At OSoMe, we uncovered a network of inauthentic accounts on Twitter that were all coordinated by the same entity. Some pretended to be pro-Trump supporters of the Make America Great Again U.S. election campaign, whereas others posed as Trump “resisters” all asked for political donations. Such operations amplify content that preys on confirmation biases and accelerate the formation of polarized echo chambers.

Curbing Online Manipulation

Understanding our cognitive biases and how algorithms and bots exploit them allows us to better guard against manipulation. OSoMe has produced a number of tools to help people understand their own vulnerabilities, as well as the weaknesses of social media platforms. One is a mobile app called Fakey that helps users learn how to spot misinformation. The game simulates a social media news feed, showing actual articles from low- and high-credibility sources. Users must decide what they can or should not share and what to fact-check. Analysis of data from Fakey confirms the prevalence of online social herding: users are more likely to share low-credibility articles when they believe that many other people have shared them.

Another program available to the public, called Hoaxy , shows how any extant meme spreads through Twitter. In this visualization, nodes represent actual Twitter accounts, and links depict how retweets, quotes, mentions and replies propagate the meme from account to account. Each node has a color representing its score from Botometer, which allows users to see the scale at which bots amplify misinformation. These tools have been used by investigative journalists to uncover the roots of misinformation campaigns, such as one pushing the “pizzagate” conspiracy in the U.S. They also helped to detect bot-driven voter-suppression efforts during the 2018 U.S. midterm election. Manipulation is getting harder to spot, however, as machine-learning algorithms become better at emulating human behavior.

Apart from spreading fake news, misinformation campaigns can also divert attention from other, more serious problems. To combat such manipulation, we developed a software tool called BotSlayer . It extracts hashtags, links, accounts and other features that co-occur in tweets about topics a user wishes to study. For each entity, BotSlayer tracks the tweets, the accounts posting them and their bot scores to flag entities that are trending and probably being amplified by bots or coordinated accounts. The goal is to enable reporters, civil-society organizations and political candidates to spot and track inauthentic influence campaigns in real time.

These programmatic tools are important aids, but institutional changes are also necessary to curb the proliferation of fake news. Education can help, although it is unlikely to encompass all the topics on which people are misled. Some governments and social media platforms are also trying to clamp down on online manipulation and fake news. But who decides what is fake or manipulative and what is not? Information can come with warning labels such as the ones Facebook and Twitter provide, but can the people who apply those labels be trusted? The risk that such measures could either deliberately or inadvertently suppress free speech, which is vital for robust democracies, is real. The dominance of social media platforms with global reach and close ties with governments further complicates the possibilities.

One of the best ideas may be to make it more difficult to create and share low-quality information. This could involve adding friction by forcing people to pay to share or receive information. Payment could be in the form of time, mental work such as puzzles, or microscopic fees for subscriptions or usage. Automated posting should be treated like advertising. Some platforms are already using friction in the form of CAPTCHAs and phone confirmation to access accounts. Twitter has placed limits on automated posting. These efforts could be expanded to gradually shift online sharing incentives toward information that is valuable to consumers.

Free communication is not free. By decreasing the cost of information, we have decreased its value and invited its adulteration. To restore the health of our information ecosystem, we must understand the vulnerabilities of our overwhelmed minds and how the economics of information can be leveraged to protect us from being misled.

Filippo Menczer is Distinguished Professor of Informatics and Computer Science and director of the Observatory on Social Media at Indiana University Bloomington. He studies the spread of disinformation and develops tools for countering social media manipulation.

Thomas Hills is a professor of psychology and director of the Behavioral and Data Science master’s program at the University of Warwick in England. His research addresses the evolution of mind and information.

Home — Essay Samples — Sociology — Fake News — The Impact of Fake News in Social Media

The Impact of Fake News in Social Media

• Categories: Fake News Social Media

About this sample

Words: 679 |

Published: Sep 1, 2023

Words: 679 | Page: 1 | 4 min read

Cite this Essay

Let us write you an essay from scratch

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

Get high-quality help

Prof Ernest (PhD)

Verified writer

• Expert in: Sociology

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

3 pages / 1410 words

4 pages / 1882 words

2 pages / 1056 words

3 pages / 1368 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Fake News

Fake news has become an alarming and pervasive issue in today's digital age, particularly within the realm of social media. The spread of misinformation and false narratives through online platforms has raised concerns about its [...]

The internet has revolutionized the way we communicate, acquire information, and conduct business. With the rise of social media platforms, people can easily share their opinions and connect with like-minded individuals. [...]

Are you ready to dive into the intriguing world of fact-checking clown purge analysis? Imagine a scenario where rumors of a nationwide clown purge sweep the internet, causing panic and fear among the public. In this essay, we [...]

Social media platforms have both positive and negative implications. Intrusive advertising, bullying and harassment, privacy threats, fake news, and violence are among the negative consequences associated with social media. It [...]

This morning started off like any other, I lit a cigarette, wrapped myself in my fuzzy blanket and started to scroll through Facebook. I saw pictures of my friend’s babies, what my grandma did at the casino last weekend, and [...]

World in this era of advanced technology and development in almost all the fields. The thing which has been recently became too problematic for all us is the “Spreading of Fake News”. Due to the upbringing that we have went [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

How should social media platforms combat misinformation and hate speech?

Subscribe to the center for technology innovation newsletter, niam yaraghi niam yaraghi nonresident senior fellow - governance studies , center for technology innovation @niamyaraghi.

April 9, 2019

Social media companies are under increased scrutiny for their mishandling of hateful speech and fake news on their platforms. There are two ways to consider a social media platform: On one hand, we can view them as technologies that merely enable individuals to publish and share content, a figurative blank sheet of paper on which anyone can write anything. On the other hand, one can argue that social media platforms have now evolved curators of content. I argue that these companies should take some responsibility over the content that is published on their platforms and suggest a set of strategies to help them with dealing with fake news and hate speech.

Artificial and Human Intelligence together

At the beginning, social media companies established themselves not to hold any accountability over the content being published on its platform. In the intervening years, they have since set up a mix of automated and human driven editorial processes to promote or filter certain types of content. In addition to that, their users are increasingly using these platforms as the primary source of getting their news. Twitter moments , in which you can see a brief snapshot of the daily news, is a prime example of how Twitter is getting closer to becoming a news media. As social media practically become news media, their level of responsibility over the content which they distribute should increase accordingly.

While I believe it is naïve to consider social media as merely neutral content sharing technologies with no responsibility, I do not believe that we should either have the same level of editorial expectation from social media that we have from traditional news media.

The sheer volume of content shared on social media makes it impossible to establish a comprehensive editorial system. Take Twitter as an example: It is estimated that 500 million tweets are sent per day. Assuming that each tweet contains 20 words on average, the volume of content published on Twitter in one single day will be equivalent to that of New York Times in 182 years. The terminology and focus of the hate speech changes over time, and most fake news articles contain some level of truthfulness in them. Therefore, social media companies cannot solely rely on artificial intelligence or humans to monitor and edit their content. They should rather develop approaches that utilize artificial and human intelligence together.

Finding the needle in a haystack

To overcome the editorial challenges of so much content, I suggest that the companies focus on a limited number of topics which are deemed important with significant consequences. The anti-vaccination movement and those who believe in flat-earth theory are both spreading anti-scientific and fake content. However, the consequences of believing that vaccines cause harm are eminently more dangerous than believing that the earth is flat. The former creates serious public health problems, the latter makes for a good laugh at a bar. Social media companies should convene groups of experts in various domains to constantly monitor the major topics in which fake news or hate speech may cause serious harm.

It is also important to consider how recommendation algorithms on social media platforms may inadvertently promote fake and hateful speech. At their core, these recommendation systems group users based on their shared interests and then promote the same type of content to all users within each group. If most of the users in one group have interests in, say, flat-earth theory and anti-vaccination hoaxes, then the algorithm will promote the anti-vaccination content to the users in the same group who may only be interested in flat-earth theory. Over time, the exposure to such promoted content could persuade the users who initially believed in vaccines to become skeptical about them. Once the major areas of focus for combating the fake and hateful speech is determined, the social media companies can tweak their recommendation systems fairly easily so that they will not nudge users to the harmful content.

Once those limited number of topics are identified, social media companies should decide on how to fight the spread of such content. In rare instances, the most appropriate response is to censor and ban the content with no hesitation. Examples include posts that incite violence or invite others to commit crimes. The recent New Zealand incident in which the shooter live broadcasted his heinous crimes on Facebook is the prime example of the content which should have never been allowed to be posted and shared on the platform.

Facebook currently relies on its community of users to flag such content and then uses an army of real humans to monitor such content within 24 hours to determine if they are actually in violation of its terms of use. Live content is monitored by humans once it reaches a certain level of popularity. While it is easier to use artificial intelligence to monitor textual content in real-time, our technologies to analyze images and videos are quickly advancing. For example, Yahoo! has recently made its algorithms to detect offensive and adult images public. The AI algorithms of Facebook are getting smart enough to detect and flag non-consensual intimate images .

Fight misinformation with information

Currently, social media companies have adopted two approaches to fight misinformation. The first one is to block such content outright. For example, Pinterest bans anti-vaccination content and Facebook bans white supremacist content. The other is to provide alternative information alongside the content with fake information so that the users are exposed to the truth and correct information. This approach, which is implemented by YouTube, encourages users to click on the links with verified and vetted information that would debunk the misguided claims made in fake or hateful content. If you search “Vaccines cause autism” on YouTube, while you still can view the videos posted by anti-vaxxers, you will also be presented with a link to the Wikipedia page of MMR vaccine that debunks such beliefs.

While we yet have to empirically examine and compare the effectiveness of these alternative approaches, I prefer to present users with the real information and allow them to become informed and willfully abandon their misguided beliefs by exposing them to the reliable sources of information. Regardless of their short-lived impact, diversity of ideas will ultimately move us forward by enriching our discussions. Social media companies will be able to censor content online, but they cannot control how ideas spread offline. Unless individuals are presented with counter arguments, falsehoods and hateful ideas will spread easily, as they have in the past when social media did not exist.

Related Content

Mary Blankenship, Carol Graham

July 6, 2020

Chris Meserole, Alina Polyakova

May 25, 2018

Clara Hendrickson

May 28, 2019

Related Books

Mark MacCarthy

November 7, 2023

Darrell M. West

May 26, 2011

Jeffrey Rosen, Benjamin Wittes

March 21, 2013

Media & Journalism Social Media

Governance Studies

Center for Technology Innovation

Courtney C. Radsch

March 25, 2024

Valerie Wirtschafter

October 26, 2023

Jessica Brandt

May 5, 2023

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

2.3: Fake News Essay

• Last updated
• Save as PDF
• Page ID 211349

Fake news is nothing new. However, recently, especially with the 2016 presidential election, the terms "fake news" and "news" has changed. Fake news is news that is not true in whole or part. It is not always easy to determine what is fake or real. However, most, if not all people, have spread fake news at least time in the past.

The way people receive, perceive, and read news has also changed, as social media has taken over people's lives. Many people get their news from social media (Kalyanam, Quezada, Poblete, \& Lanckriet, 2016). With the Russian interference with the 2016 U.S. election, fake news has come to the forefront of news' credible due to social media sites. Write an essay of at least three pages, in which you argue what is fake news or how news has changed. In the essay, you must define your definition of "news" and "fake news." You may take this essay in any direction on any aspect relating to fake news. This essay needs to be in MLA or APA format with the font type Times New Roman and font size 12.

Possible topics may focus on the following below, but they are not limited to:

• Think of the seven techniques of propaganda (bandwagon, name calling, glittering generalities, transfer, testimonial, plain folks, and card stacking). Then, choose a news story from the front page of major newspaper (e.g., The New York Times) to construct the essay around it.
• Discuss how social media (e.g., Facebook) has changed the way people get news and perceive what news is.
• Does the press still have freedom of speech?
• How has the \#metoo movement affected how society looks at news? Think about how the \#metoo movement has affected Bill Cosby, Harvey Weinstein, Brett Kavanaugh, and other high executives, who have been accused of sex-related crimes.
• How has the White House Correspondent's Dinner changed during the Trump presidency?
• If President Trump tweets, is that news? Why or why not? https://www.nytimes.com/2016/11/29/b...ews-media.html
• How has the profession of journalism changed in the Trump presidency?
• Discuss how words have real-life consequences. https://www.cnn.com/2018/10/24/media/trump-media-attacks-haveconsequences/index.html
• How does a misrepresentation of news (e.g., Boston Marathon bomber [2013; see links below] and Navy Yard shooter [2013; see links below]) by a journalist affect how news is viewed by society? http://www.usatoday.com/story/money/columnist/rieder/2013/04/18/media-bostonfiasco/2093493 http://www.usatoday.com/story/news/2013/09/16/networks-retract-id-of-dc-navy-yard-shooter/2821329 http://www.usatoday.com/story/money/columnist/rieder/2013/09/16/media-mistakes-in-coverage-of-dc-navy-yard-shooting/2822551/ http://www.washingtonpost.com/blogs/erik-wemple/wp/2013/09/16/washingtons-fox-station-tweets-navy-yard-events-from-police-scanner/ In  http://chronicle.com/blogs/percolator/major-fraud-plea-has-university-scientists-regretting-journal-article/33713 https://www.ted.com/talks/stephanie_busari_how_fake_news_does_real_harm https://www.nytimes.com/2017/03/24/technology/london-terror-attack-suspect-social-media.html 

This essay can be combined with the Final Paper. If it is combined with the Final Paper, the page requirements for both assignments must meet the combined total of the minimum pages for both.

This means that the 3 pages for the Fake News essay and the 5 pages for the Final Paper need to equal a minimum of 8 pages.

Kalyanam, J., Quezada, M., Poblete, B., \& Lanckriet, G. (2016). Prediction and characterization of high-activity events in social media triggered by real-world news. PLoS One, 11(12), DOI:10.1371/journal.pone.0166694

ESSAY SAUCE

FOR STUDENTS : ALL THE INGREDIENTS OF A GOOD ESSAY

Essay: Impact of fake news (focus on Philippines)

Essay details and download:.

• Subject area(s): Media essays
• Reading time: 3 minutes
• Price: Free download
• Published: 28 February 2022*
• File format: Text
• Words: 900 (approx)
• Number of pages: 4 (approx)
• Tags: Fake news essays

Text preview of this essay:

This page of the essay has 900 words. Download the full version above.

In this century, where innovations and advancement of technology is prominent, there are also issues and problems that comes along with it. As technology continues to develop, social media plays a role in educating and voicing out the concerns of millions of people. Social media can be associated with a double-edged knife. We benefit from accessing information quickly, and there is no limitation on who we can connect to. It is one way of interacting with other people to share thoughts and ideas with. However, what will happen if the information we are getting are no longer truthful? It is truly necessary to be aware and vigilant of the things we see online and confirm these first before believing in them. Indeed, fake news has its negative effects on building a sense of nationalism among Filipinos.

Fake news are stories that are false wherein the story itself is fabricated, with no verifiable sources, quotes, or facts. In line with this, nationalism is the sense of unity among citizens and it is a movement that promotes a nation’s best interest and it is greatly affected by the spread of fake news in the country. In building our sense of nationalism, we have to be one, as citizens, and our democracy shouldn’t be tainted.

Incidentally, the media is often accused of spreading fake news . There is growing evidence on attacks on media credibility and a sharp increase in the number of journalists imprisoned on false news charges (Lees, 2018). In the Philippines, President Duterte labelled a particular media outlet as “fake news” and even announced that he would revoke said media’s operating license. This is really alarming since the President had no evidence on such accusations and felt personally attacked by the published articles of the media outlet which caused his outburst. These attacks on press freedom has its impact on the society at large. It is the job of the media to report necessary and relevant information to the people as this would help them see and understand the happenings in the society. According to Civicus (n.d.), when authorities discredit the media, disagreements happen between citizens which may result to protests and violence. These fake news against media will prevent Filipinos from strengthening their sense of nationalism since they are fighting their own fellowmen and it is opposite to the essence of nationalism which is to create unity among citizens.

Aside from this, sharing of fake news is not only limited to the media, a lot of trolls on social media are beginning to multiply and the number of people who actually believe in this fake news is troubling. This is because people are believing more in this fake information rather than credible sources and they would make decisions based on false assumptions (Quilinguing, 2019). One example of this situation is the Digong Duterte Supporters (DDS), as the pandemic continues to take its toll, these supporters keeps on creating heated arguments on social media. They keep on sharing false information about other politicians who disagree with President Duterte’s actions. On the other hand, it also finds its way to affect democracy in the Philippines. Since social media is a way for Filipinos to have a voice, with the presence of fake online accounts, they can be silenced by the latter. They may manipulate public opinion to favor the interests of a particular group (Quilinguing, 2019). In this case, the oppressed remains oppressed and they lose their freedom in making their sentiments heard. This prevents the people from building their nationalism as they are being deprived of their freedom to speak their minds and it also causes conflicts in the people of society which contradicts the meaning of nationalism in terms of having unity.

Another impact of fake news that spreads through misinformation has its way in constructing stereotypes and further discrimination among a particular sector of the society. Grambo (2019) stresses that fake news is divisive as it may negatively portray ethnic or religious group as people who are unworthy of citizenship or they may even dehumanize these individuals. In the Philippines, there is an existing dueling groups: DDS and dilawan. Just because they do not agree with the actions done by the President, they are immediately labeled as such. In addition, these groups often exchange hurtful and personal attacks with each other. Fake news is usually the reason why both groups battle each other which in turn widens the gap between them. These situations only add fuel to the existing problems in the society and people would have no unity since they do not consider their fellowmen as their equal and they would have a hard time in building nationalism among themselves since its essence is to create oneness among the citizens and uphold the democracy of each individual.

Ultimately, nothing good comes out of spreading fake news. It will greatly impact how news are presented, how decisions are made, how people are treated, and it facilitates discrimination among Filipinos. To reiterate, it cannot be denied that social media has greatly benefited us in our daily lives. However, as fake news continues to progress, we must always check the credibility of the information we read before we believe them. We must inform and educate others as well if they become victims of these false information. To start, we should stop encouraging the spread of fake news.

2020-9-21-1600675349

...(download the rest of the essay above)

Discover more:

• Fake news essays

Related essays:

• Fake and biased news, and its impact on society
• Deepfakes – history, examples, uses and risks

Recommended for you

• Is the gatekeeper concept applicable for the journalistic practice in the Czech Republic?
• Consequences of fake news and deep fakes

About this essay:

If you use part of this page in your own work, you need to provide a citation, as follows:

Essay Sauce, Impact of fake news (focus on Philippines) . Available from:<https://www.essaysauce.com/media-essays/impact-of-fake-news-focus-on-philippines/> [Accessed 02-04-24].

These Media essays have been submitted to us by students in order to help you with your studies.

* This essay may have been previously published on Essay.uk.com at an earlier date.

Essay Categories:

• Accounting essays
• Architecture essays
• Business essays
• Computer science essays
• Criminology essays
• Economics essays
• Education essays
• Engineering essays
• English language essays
• Environmental studies essays
• Essay examples
• Finance essays
• Geography essays
• Health essays
• History essays
• Hospitality and tourism essays
• Human rights essays
• Information technology essays
• International relations
• Leadership essays
• Linguistics essays
• Literature essays
• Management essays
• Marketing essays
• Mathematics essays
• Media essays
• Medicine essays
• Military essays
• Miscellaneous essays
• Music Essays
• Nursing essays
• Philosophy essays
• Photography and arts essays
• Politics essays
• Project management essays
• Psychology essays
• Religious studies and theology essays
• Sample essays
• Science essays
• Social work essays
• Sociology essays
• Sports essays
• Types of essay
• Zoology essays

From Russia, Elaborate Tales of Fake Journalists

As the Ukraine war grinds on, the Kremlin has created increasingly complex fabrications online to discredit Ukraine’s leader and undercut aid. Some have a Hollywood-style plot twist.

Credit... Devin Oktar Yalkin for The New York Times

Supported by

• Share full article

By Steven Lee Myers

• Published March 18, 2024 Updated March 21, 2024

A young man calling himself Mohamed al-Alawi appeared in a YouTube video in August. He described himself as an investigative journalist in Egypt with a big scoop: The mother-in-law of Ukraine’s president had purchased a villa near Angelina Jolie’s in El Gouna, a resort town on the Red Sea.

Listen to this article with reporter commentary

Open this article in the New York Times Audio app on iOS.

The story, it turned out, was not true. Ukraine denied it, and the owner of the villa refuted it. Also disconnected from reality: Alawi’s claim to being a journalist.

Still, his story caromed through social media and news outlets from Egypt to Nigeria and ultimately to Russia — which, according to researchers, is where the story all began.

The story seemed to fade, but not for long. Four months later, two new videos appeared on YouTube. They said Mohamed al-Alawi had been beaten to death in Hurghada, a town about 20 miles south of El Gouna. The suspected killers, according to the videos: Ukraine’s secret service agents.

These claims were no more factual than the first, but they gave new life to the old lie. Another round of posts and news reports ultimately reached millions of internet users around the world, elevating the narrative so much that it was even echoed by members of the U.S. Congress while debating continued military assistance to Ukraine.

Ever since its forces invaded two years ago , Russia has unleashed a torrent of disinformation to try to discredit Ukraine’s leader, Volodymyr Zelensky, and undermine the country’s support in the West.

This saga, though, introduced a new gambit: a protracted and elaborately constructed narrative built online around a fictitious character and embellished with seemingly realistic detail and a plot twist worthy of Netflix.

“They never brought back a character before,” said Darren Linvill, a professor and director of the Media Forensics Hub at Clemson University, who has extensively studied Russian disinformation.

The campaign shows how deftly Russia’s information warriors have shifted to new tactics and targets as the war in Ukraine has dragged on, just as Russian forces on the ground in Ukraine have adjusted tactics after devastating battlefield losses.

Groups with ties to the Kremlin continue to float new narratives when old ones fail to stick or grow stale, using fake or altered videos or recordings and finding or creating new outlets to spread disinformation, including ones purporting to be American news sites .

A video appeared on TikTok last month claiming to show a Ukrainian doctor working for Pfizer accusing the company of conducting unlawful tests on children. On the social network X, a man claiming to be an associate producer for Paramount Pictures spun a tale about a Hollywood biopic on Mr. Zelensky’s life.

The tale attributed to Mohamed al-Alawi is not even the only baseless allegation that Mr. Zelensky had secretly purchased properties abroad using Western financial assistance. Other versions — each seemingly tailored for a specific geographic audience — have detailed a mansion in Vero Beach, Fla., and a retreat in Germany once used by Joseph Goebbels, the Nazi minister of propaganda.

The Russians have “demonstrated adaptability through the war on Ukraine,” Microsoft wrote in a recent report that disclosed Russia’s fraudulent use of recorded messages by famous actors and celebrities on the Cameo app to try to smear Mr. Zelensky as a drug addict.

Even when debunked, fabrications like these have proved exceedingly difficult to extinguish entirely.

YouTube took down the initial video of the character Mohamed al-Alawi, linking it to two other accounts that had previously violated the company’s policies. The accusation still circulates, however, especially on platforms, like X and Telegram, that experts say do little to block accounts generating inauthentic or automated activity. Some of the posts about the video appear to have used text or audio created with artificial intelligence tools; many are amplified by networks of bots intended to create the impression that the content is popular.

What links the narratives to Russia is not only the content disparaging Ukraine but also the networks that circulate them. They include news outlets and social media accounts that private and government researchers have linked to previous Kremlin campaigns.

“They’re trolling for a susceptible (and seemingly abundant) slice of citizens who amplify their garbage enough to muddy the waters of our discourse, and from there our policies,” said Rita Katz, the director of the SITE Intelligence Group, an American company that tracks extremist activity online and investigated the false claims about the villa.

The Making of a Fake Journalist

The video first appeared on Aug. 20 on a newly created YouTube account that had no previous activity and almost no followers, according to the Institute of Strategic Dialogue, a global nonprofit research organization in London, which traced the video’s spread.

The man appeared in a poorly lit room reading from his computer screen, which was reflected in his thick glasses. He appeared to be a real person, but it has not been possible to verify his actual identity. No one by the name of Mohamed al-Alawi appears to have produced any previous articles or videos, as would be expected of a journalist. According to ActiveFence, an internet security company, the character has no educational or work history, and no network of friends or social connections online.

The video, though, showed what purported to be photographs of a purchase contract and of the villa itself, creating a veneer of authenticity for credulous viewers. The property is, in fact, part of a resort owned by Orascom Development, whose website highlights El Gouna’s “year-round sunshine, shimmering lagoons, sandy beaches and azure waters.”

An article about the video’s claim appeared two days later as a paid advertisement, or branded content, on Punch, a news outlet in Nigeria, as well as three other Nigerian websites that aggregate news and entertainment content.

The article had the byline of Arthur Nkono, who according to internet searches does not appear to have written any other articles. The article quoted a political scientist, Abdrulrahman Alabassy, who likewise appears not to exist except in accounts linking the villa to the corrupt use of Western financial aid to Ukraine. (Punch, which later removed the post, did not respond to requests for comment.)

A day later, the claim made its first appearance on X in a post by Sonja van den Ende, an activist in the Netherlands, whose articles have previously appeared on propaganda outlets linked to the Russian government, according to the Institute for Strategic Dialogue. (She also served as an election observer in an occupied territory of Ukraine during Russian parliamentary elections in September.)

Within days, reports about the villa appeared on X in French and Romanian, and in English on three different Reddit forums.

According to Roberta Duffield, director of intelligence for Blackbird.AI, an internet security company, nearly 29 percent of the accounts amplifying the reports appeared to be inauthentic bots, an unusually high number that would normally indicate a coordinated campaign.

Eight days after the video appeared, Russia state television networks like Channel One, Rossiya 24 and RT (in Arabic and German) reported it as a major revelation uncovered by a renowned Egyptian investigative journalist.

The story seemed to stall there. Naguib Sawiris, the scion of the Egyptian family that owned the development, curtly denied the sale in a reply on X.

And no more was heard from or about the character called Mohamed al-Alawi — until late December.

That was when two new videos emerged on a YouTube channel called “Egypt News,” claiming that he was dead.

The channel had been created the day before. One video showed a man identified as Alawi’s brother, Ahmed, answering questions from another man.

The police, he said, told him that they suspected his brother had been beaten to death by “Ukrainian special forces who acted on behalf of President Zelensky or another high-ranking official.”

He spoke with his hand cupped over his face to obscure his identity. The other video showed what was said to be the site of an attack, though the images were indistinct. “I can’t tell you anything else,” he said in the video, which YouTube later removed. “I’m afraid for my family.”

The video also tried to explain away some of the obvious holes in the initial story, including why there was no evidence online of Alawi’s previous work. “It was his first big assignment,” the man said.

The new episode spread as the first video had. A day later, an article about the death appeared on an obscure website created last year called El Mostaqbal, a name similar to but unrelated to the actual news organization in Lebanon.

“A reporter who announced that Zelensky’s mother-in-law brought a luxury villa has died under mysterious circumstances,” the headline read. Other reports that followed dropped any uncertainty and began referring to his “murder.”

In fact, Egypt’s Ministry of the Interior said there were no reports or evidence that anyone resembling the man in the video had been “subjected to harm.” The statement went on to note that the property itself had not been sold.

Still, according to the Institute for Strategic Dialogue, posts about the supposed killing were viewed a million times on X on Dec. 25.

It also appeared on the website of the Middle East Monitor, or MEMO, operated by a well-known nonprofit organization in London and financed by the government of Qatar. A journalist who once reported from Moscow for The Telegraph of London, Ben Aris, cited it at length on the platform, though, when challenged, he said he had just made note of the rumor. “I don’t have time to check all this stuff myself,” he wrote.

It appeared in English on a site, Clear Story News, that Mr. Linvill of Clemson’s Media Forensics Hub had previously linked to Russia’s disinformation efforts. (The site lists no contact information)

Mr. Linvill described the process as a form of “narrative laundering” — moving false claims from unknown or not credible sources to ones that, to the unwitting at least, seem more legitimate.

More Elaborate Narratives

The Institute for Strategic Dialogue studied three other complex narratives about Ukraine, as well.

One featured a French journalist who claimed that the son of George Soros — a regular target of Russian and far-right political attacks — had secretly acquired land for a toxic waste dump in Ukraine. An unnamed doctor in Africa said in another that an American medical charity, the Global Surgical and Medical Support Group, was harvesting the organs of wounded Ukrainian soldiers for transplants for NATO officers.

Then there was the case of a man calling himself Shahzad Nasir, whose profile on X identifies him as a journalist with Emirates 24/7, an English-language news outlet in Dubai, though he has no apparent bylines on the site.

In November, he claimed that cronies of Mr. Zelensky bought two yachts — Lucky Me and My Legacy — for $75 million. His evidence, like Mohamed al-Alawi’s, includes photographs of the vessels and purported purchase agreements.

In fact, as the BBC documented in December, the yachts had not been purchased and remained for sale. Despite numerous efforts by fact checkers to dispel it as rumor, the claim circulated extensively.

Last month, the character Nasir reappeared in another video. This time he had a new version of the tale, claiming that the purchases had been scuttled after he exposed the secret deal.

The ramifications of these campaigns are difficult to measure precisely. There are signs, though, that they resonate even when proved false.

Senator J.D. Vance, a Republican of Ohio and an outspoken critic of Ukraine aid, seemed to embrace the claim in December during an interview on “War Room,” the podcast hosted by Stephen K. Bannon, the onetime adviser to former President Donald J. Trump.

“There are people who would cut Social Security — throw our grandparents into poverty — why?” Mr. Vance said. “So that one of Zelensky’s ministers can buy a bigger yacht?”

That prompted a public rebuke this month from a Republican colleague, Senator Thom Tillis of North Carolina, who ridiculed those who repeat unproven allegations.

“They’ve heard somebody say that if we pass this bill, that we’re all going to go ride to Kyiv with buckets full of money and let oligarchs buy yachts!” he said of critics of the assistance to Ukraine, in what he later called a reference to Mr. Vance’s comments. “I wonder how the spouses of the estimated 25,000 soldiers in Ukraine who have died feel about that? I mean, really, guys?”

Karoun Demirjian contributed reporting.

Audio produced by Parin Behrooz .

An earlier version of this article misstated, in one reference, the name of a group at Clemson University that studies disinformation. It is the Media Forensics Hub, not the Digital Media Hub.

How we handle corrections

Steven Lee Myers covers misinformation for The Times. He has worked in Washington, Moscow, Baghdad and Beijing, where he contributed to the articles that won the Pulitzer Prize for public service in 2021. He is also the author of “The New Tsar: The Rise and Reign of Vladimir Putin.” More about Steven Lee Myers

Our Coverage of the War in Ukraine

News and Analysis

Ukraine’s troop-starved brigades have started their own recruitment campaigns  to fill ranks depleted in the war with Russia.

The Czech Republic froze the assets of two men and a news website  it accused of running a “Russian influence operation” in Europe.

Ahead of the U.S. elections, Russia is intensifying efforts to elevate candidates  who oppose aid for Ukraine and support isolationism, disinformation experts say.

Symbolism or Strategy?: Ukrainians say that defending places with little strategic value is worth the cost in casualties and weapons , because the attacking Russians pay an even higher price. American officials aren’t so sure.

Elaborate Tales: As the Ukraine war grinds on, the Kremlin has created increasingly complex fabrications online  to discredit Ukraine’s leader, Volodymyr Zelensky, and undermine the country’s support in the West.

Targeting Russia’s Oil Industry: With its army short of ammunition and troops to break the deadlock on the battlefield, Kyiv has increasingly taken the fight beyond the Ukrainian border, attacking oil infrastructure deep in Russian territory .

How We Verify Our Reporting

Our team of visual journalists analyzes satellite images, photographs , videos and radio transmissions  to independently confirm troop movements and other details.

We monitor and authenticate reports on social media, corroborating these with eyewitness accounts and interviews. Read more about our reporting efforts .

Advertisement

Today's Paper | April 02, 2024

Minister terms fake news on social media ‘biggest challenge’.

ISLAMABAD: Federal Minister of Information and Broadcasting Attaullah Tarar on Thursday said disinformation and fake news are the problems of entire world which should be controlled.

During a meeting with the political counselor of the British High Commission, Zoe Ware, the minister said the biggest challenge on social media was fake news.

Referring to the restrictions on social media platform X (former Twitter), the minister said the government was trying to deal with this problem.

He said there was a global need to develop a code of conduct for social media and for content creators.

Ms Ware congratulated the PML-N on forming the government and Mr Tarar on assuming the charge as minister of information and broadcasting.

The British diplomat assured Mr Tarar of her government’s full cooperation for promotion of bilateral relations between Pakistan and UK.

Published in Dawn, March 29th, 2024

X already banned when new govt took over: Information Minister Tarar

Interior minister calls for revisiting social media laws

X banned in country since feb 17 on spy agencies’ reports, shc told.

ڈان انویسٹی گیشن: بحریہ ٹاؤن کراچی کی مسلسل غیرقانونی توسیع کی مشکوک کہانی

سکندر اعظم نے کس بادشاہ کی موت پر کہا کہ ایک بادشاہ کو اس طرح نہیں مرنا چاہیے؟

‘زخم اور ہڈی ٹوٹی ہوتی تو بریانی کیسے بناتا؟’، انور مقصود کی افواہوں کی تردید

Has Toshakana Case Failed To Withstand The Judicial Scrutiny?

Israel Declares War on Iran

Arthur Balfour Was A Raging Anti-semite: Zachary Foster

Why Has Pakistan Historically Kept PKR Overvalued?

Internet Blocks Threaten Pakistan’s Creator Economy

Week’s Must-Know Headlines: Another State Dept Official Resigns Over Govt’s Stance On Gaza Conflict

How Is World Reacting To ICJ Order On Gaza Aid?

Market Wrap Up: KSE-100 Index Closes At 67,005.10 Points

Dear visitor, the comments section is undergoing an overhaul and will return soon.

Latest Stories

Senate polls under way except in KP amid oath-taking dispute

IHC judges’ letter: PTI rejects ‘like-minded’ bench, demands full court

Israel leaves Gaza’s largest hospital in ruins

Despite flak, Nawaz chairs another government meeting

Israel ‘flattens’ Iranian embassy in Damascus

Chaos in NA as opposition stopped from debating IHC judges’ letter

Filmmaker Mahera Omar’s documentary wins award at Nepal film festival

Trying out four very different iftar menus in Islamabad

Pakistanis will finally be able to watch The Glassworker in theatres this summer

Most popular.

Pakistan ‘unlikely to break free from IMF support, incomplete PM terms’, says IIF

Pakistan on brink of default due to tax evasion, says Asif

Babar Azam returns as skipper as PCB hands him T20, ODI captaincy in ‘strategic move’

Govt hikes petrol price by Rs9.66/litre

Dawn Investigations: Bahria Town investors — road to nowhere

QAU — university or jail?

Two troops martyred in Gwadar gun attack

Cartoon: 30 March, 2024

IHC judges’ letter: Over 300 lawyers call on SC to take notice under Article 184(3)

How do the Senate elections work?

180 days of protest: Why are the Pakhtuns in Chaman encamped on the Pak-Afghan border?

Editorial: Jillani’s recusal strengthens the argument that judiciary must address letter issue internally

Arrest of two serving CMs on election eve has revitalised a foundering campaign to dislodge Modi

While pressure may ease on some in judiciary for a bit, little will change for others

Is there now a winning team?

The IHC six

Autism and SDGs

Pro-poor reform

Fighting monopolisation

Letter inquiry

Expanding the net, katcha badlands.

Government support?

Biden’s letter, family sins, strikes hit gaza as truce talks set to resume in cairo, doha.