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How to Write a CCOT Essay

Last Updated: March 31, 2023 References

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The Continuity and Change-Over-Time (CCOT) essay is a type that is commonly used on the AP World History exam, but you may be asked to write one for other settings or courses. Basically, it asks you to think about how a particular subject has developed or altered over time, as well as to consider what about it has stayed the same. Writing one is a piece of cake if you practice, organize your thoughts before you start writing, and keep the basic requirements of this essay type in mind as you work.

Gathering the Information you Need

• For example, a prompt might ask you to “Evaluate the extent of continuity and change in the lives of European immigrants in the United States between 1876 and 1918."

• A thesis might look something like “While the United States maintained its position as a manufacturing superpower in the first five decades after WWII, over time its focus shifted from the production of traditional goods to the creation of innovative electronics.”
• Your thesis must specifically mention the time period. [2] X Research source
• Be as direct as possible about the continuity and change over time.

• Jot down the basic facts you know about the topic, as well as any key details that stick out in your mind. Build your essay around these concrete details.
• For example, if your topic asks you to consider the development of manufacturing in the United States after WWII, start by writing down what you know about industries during that time period. If you know particular details about developments in the auto industry, you might work with those above all.

• For instance, if you are writing about changes and continuities in manufacturing after WWII, you might argue that the Vietnam War and the fuel crisis of the 1970s decisively changed the shape of American industries.
• Create a timeline of events to sort out your historical information in order and help identify the turning point.

• Continuing with the manufacturing example, your might keep in mind that the fuel crisis of the 1970s led to a rise in imported, fuel-efficient cars, which impacted the auto industry in the United States.

Organizing and Writing Your Essay

• Create an outline to serve as the framework for your essay.
• For example, you might have a first paragraph that establishes your thesis and the conditions at the start of the time period. This could be followed by two paragraphs on changes that occurred over time. Finally, the essay could close with a paragraph on what stayed the same.

• Be selective and organize your thoughts. Don’t just dump out everything you know about a subject.
• Stay in the place and time relevant to the prompt. If a prompt asks you to think about the development of manufacturing in the United States, your evidence shouldn’t be based on examples from manufacturing in China.
• On the other hand, don’t forget to explain the global significance of your topic. For instance, at some point in your essay, you might note that changes in manufacturing in the United States led to an increasing rise in imported home goods manufactured in China.

• If you get stuck, go back to the notes/ideas you had about the turning point, since this can show you exactly what changed, when, and why.

Preparing for the Exam

• 1 point for having an acceptable thesis
• Up to 2 points for addressing all parts of the question
• Up to 2 points for effectively using appropriate historical evidence
• 1 point for explaining the changes in terms of world history and global contexts
• 1 point for analyzing the process of continuity and change over time

• For example, including several relevant examples of changes and continuities will help you earn points in the Expanded Core.
• You cannot earn points in the Expanded Core unless your essay already covers all aspects of the Basic Core.

• Don’t use evidence from the wrong time period. Using the example of Captain Cook won’t help you if the essay asks you to examine continuities and changes in work exploration between 1400 and 1700.
• Avoid vagueness of dates. Don’t write an essay that discusses “exploration before the eighteenth century.” Instead, tell your readers that your essay will discuss changes and continuities from 1492 (Columbus’ first voyage) to Hudson’s voyage of 1609-1611.
• Don’t just dump as many facts as possible into your essay. Organize and analyze all information you include, making sure it is relevant.

Community Q&A

• If you are writing a CCOT essay for a class, always ask your teacher for advice. He or she may have specific expectations for the essay that may differ from these general guidelines. Thanks Helpful 0 Not Helpful 0
• If you can't spell a word, choose another one. Although spelling mistakes and grammar errors would not deduct points, poor spelling can cast a shadow on the rest of your essay. Thanks Helpful 0 Not Helpful 0
• Avoid cursive, especially if your handwriting is messy, as it may be harder for the AP essay readers. Don't have too many scribbles on the paper, for they may make it harder for the grader to read. Thanks Helpful 0 Not Helpful 0
• Keep an eye on your time. You do not want to spend too much time on this one essay, running out of time on the rest. Thanks Helpful 0 Not Helpful 0

Other wikiHows

• ↑ http://www.utdallas.edu/apsi/documents/cone-2016.pdf
• ↑ http://highered.mheducation.com/sites/dl/free/0024122010/899891/AP_World_History_Essay_Writers_HB.pdf
• ↑ https://gibaulthistory.files.wordpress.com/2011/04/possible-ccot-essay-structures.pdf
• ↑ http://apcentral.collegeboard.com/apc/members/courses/teachers_corner/40896.html
• ↑ https://secure-media.collegeboard.org/digitalServices/pdf/ap/apcentral/ap15_world_history_q3.pdf
• ↑ http://worldhistoryconnected.press.illinois.edu/6.2/cohen.html

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Continuity and Change Over Time in the AP Histories

24 min read • may 15, 2022

William Dramby

The one thing you need to know about this historical reasoning skill:

College Board Description

Reasoning processes describe the cognitive operations that students will be required to apply when engaging with the historical thinking skills on the AP Exam. The reasoning processes ultimately represent the way practitioners think in the discipline. Specific aspects of the cognitive process are defined under each reasoning process.

Identify patterns of continuity and/or change over time.

Describe patterns of continuity and/or change over time.

Explain the relative historical significance of specific historical developments in relation to a larger pattern of continuity and/or change.

Organizing Question

How have individuals and societies changed over time and how have they stayed the same? Why?

Continuities and Change Over Time

How have you changed since you were younger? This is a pretty easy question. You have physically grown, you matured both academically and socially, and you found new hobbies, interests and activities that are age-appropriate.  Historians look for change over time. We look for how societies became wealthier, how empires fell, and the roles of different social groups changed.  

However, how have you stayed the same since you were younger?  Asking about continuities in your personality and your life is harder.  Continuities are not as obvious. Some still have a love for Star Wars movies while others will always want to play a pick-up game of basketball. Historians look for continuities over time. We look for how religion continued to play a role in peoples’ lives, how societies continued to be patriarchal, and how ideas like liberty and freedom persist.

When students study world history, they study the changes and continuities over time (CCOT). AP World History has had a rich history of asking students to write CCOT essays and use the skills in attacking stimulus-based multiple-choice questions.  

Period 1 (1200 to 1450)

1200-1450 changes.

⚡ Increase of trade along the Silk Road because of Mongol conquests and because of new stable powers

The Mongols were a nomadic tribe originating from modern day Mongolia who quickly spanned across nearly all of Eurasia, stretching from the Middle East to the eastern coast of China. In fact, the only places that were successful in fighting off the Mongols were Japan (who were aided by frequent typhoons) and India. Though short lived, an important effect of the Mongol Empire was the reunification of the Silk Roads. Prior to 1200, the Silk Roads were generally dangerous and not as prosperous as growing sea trade like in the Indian Ocean. However, the Mongols unified the Silk Roads and made it safer and easier for them to navigate.

The Mongols created Pax Mongolica or Peace of the Mongols. Trade was protected from the Mediterranean to the South China Sea. Cities like Samarkand emerged built upon trade and routes like the Trans-Saharan and Indian Ocean Trade Networks were all linked.

⚡New technologies spread like astrolabe and magnetic compass increasing exploration and trade

As empires like the Abbasid Caliphate grew across the Middle East and China grew in East Asia, new technologies were created explicitly for the functions of trade and navigation. The astrolabe , created in the Islamic World, aided travelers in using the stars to navigate (Fun fact, you can still buy astrolabes today! Though they are a bit pricey).

The Baghdad House of Wisdom is a famous example of academics and intellectualism in Dar-al-Islam, such as new innovations in algebra and trigonometry. Similarly, Song China saw a boom in innovation and new products. New forms of paper grew, leading to flying money , which we’ll discuss later, and most importantly the magnetic compass became a commonly used navigational tool.

Maritime trade growth during the period 1200 - 1450 fueled most of the technological innovation. New boats became widespread along sea trade routes. Arab dhows were ships with triangular lateen sails that were widespread in the Islamic world. Similarly, Chinese junks were small ships that traveled west from China.

⚡Buddhism spread and morphed from Northern India to Tibet, China, Southeast Asia, and Japan

Though it started in Northern India around 600 BCE, Buddhism eventually spread over the Himalayan Mountains traveling along trade routes and by various missionaries to other Asian lands. However, each region will impact the eventual form of Buddhism thus Theravada, Mahayana, and Vajrayana Buddhism emerge. 

Many comparisons can be drawn between Buddhism and Christianity, which are both religions that spread throughout this period. For example, Christianity is a proselytizing religion, which means it seeks out converts, whereas Buddhism is not. Buddhism and Christianity also both saw significant cultural diffusion and cultural blending during this time period.

⚡The Aztecs and Inca emerged as large empires in Mesoamerica and South America, respectfully

Before their eventual conquest by the Spanish Conquistadors, the Aztecs and Inca were large, thriving empires that united the peoples of Mesoamerica and South America politically, economically, and socially. While the Aztecs and Inca empires were large, complex political structures that we cannot do justice in just a few short paragraphs, there are some must-know things about the Aztecs and Incas.

The Aztecs are known especially for their architecture, such as pyramids and sacrificial/monumental architecture. They also had chinampas , large island-like farmlands that floated on water. Politically, the empire used the tribute system , where smaller conquered areas paid tribute for protection.

The Incas used the mita system , a system established by the Inca Empire in order to construct buildings or create roads throughout the empire. It was later transformed into a coercive labor system when the Spanish conquered the Inca Empire. They’re also well known for their terrace agriculture such as the stunning Machu Picchu .

⚡ Economic powers emerged like Mali Kingdom and Delhi Sultanate

Regions that lay outside Christiandom and Dar al-Islam are uniting politically, economically, and socially. The Mali Kingdom and Delhi Sultanate were both wealthy and powerful empires that saw Islam as a uniting factor.

In fact, Mali was one of the most wealthy nations in all of history, with Mansa Musa , a Mali king, being the most wealthy person in all of human history. Mali and it’s capital city Timbuktu unified the Sahara and created the Trans-Saharan Trade Route.

⚡ Trade saw new economic and financial developments

As a result of the growth of interregional trade, new financial tools were created to aid in the transfer of goods across borders. Paper money , nicknamed “flying money” was a new innovation that came from China. Further, credit became a new tool of borrowing money that aided in financial asset growth.

1200-1450 Continuities

🔗China continued to be largely a Confucian society

Confucianism has had a large influence on the culture of China since before the Qin Dynasty. Between its influence on social structure such as filial piety and political structure such as the Five Relationships, no other philosophy has so impacted China. Confucianism emphasized education and a strong bureaucracy for the Chinese government, leading to a unique political structure.

The Civil Service Exam system from the Qin Dynasty was strengthened in Tang / Song China enabling a bureaucracy built on merit and not necessary hereditary lines to develop. However, while meritocratic in theory , wealth allowed people to get tutors and special classes to learn the tests, leading to social stratification still.

🔗Patriarchy remained a strong social force across the globe

Throughout history, one of the most consistent social forces has been patriarchy . In this time period, despite there being some advances in women’s rights, specifically in the Islamic world, patriarchy continued to place men above women in the social pyramid. 

Patriarchy is one of the most important continuities throughout history, and will follow social structures not just in the post-classical era, but in essentially every part of history that you learn.

🔗Trade continued to be the primary form of economic interaction 

Trade saw many changes during this time period, as we’ve outlined above, but nevertheless, comparing the post-classical era to the classical era, trade continued to form the basis.

Period 2 (1450-1750)

1450-1750 changes.

⚡ Western Europe faced the Protestant Reformation seeing the rise of regional Christian churches and the power of the Roman Catholic Church decrease

Martin Luther challenged the role of the Roman Catholic Church in Christianity. He promoted a more personal relationship with God and the word . From his 95 Theses , will come a radical shift in European Christianity. Luther found the Catholic policy of indulgences to be the primary signal of corruption within the church, along with a host of other issues.  

New Protestant Churches emerger like the Lutheran and the Church of England while religious wars also inflame France and Germany. Other churches and sects of Christianity, like the Calvinists, will see expansions into the Americas in the 1600s. Religious wars like the Thirty Years’ War sprung up across Europe as well.

⚡ Weakening of the Roman Catholic Church occurs throughout this era with the Reformation, Scientific Revolution, and Enlightenment increasing the popularity of humanism and empiricism

The Protestant Reformation was aided by the Scientific Revolution , a movement that helped spawn higher intellectualism in Europe (though it must be noted that many of the discoveries of the Sci. Rev. either had been discovered or were aided by discoveries that had been made in the Islamic World in previous years). Important developments were advancements in physics, biology, and the development of the formal scientific method . Scientists like Copernicus and Galileo were important astronomers who helped prove astronomical facts regarding orbits. 

The Enlightenment came a bit after the Scientific Revolution, with the Enlightenment being more of a philosophical movement rather than strictly a scientific one (though science was still part!). The Enlightenment brought with it many philosophies that we still reference today such as capitalism , formally coined by Adam Smith in The Wealth of Nations , and new political theories like the separation of powers (Montesquieu), the social contract (Rousseau), and natural rights of life, liberty, and property (John Locke). The Enlightenment marked a shift in philosophy from religiosity to more of a secular form of thinking such as rationalism and empiricism .

⚡ Islamic world of Dar al-Islam expanded into large land-based empires that stretched from Europe through South Asia converting people, increasing trade connections, and forming new syncretic beliefs

The Ottoman Empire, Safavid Dynasty, and Mughal Empire all developed strong land-based empires that brought people of different languages and faiths together while also strengthening their unity under Islam. The spread of these empires was very much so due to guns , which were a new invention created after the creation of gunpowder in Song China. For example, the Ottoman Empire was able to blast through the walls of Constantinople to easily take over in 1453.

These empires developed complex political and social structures such as the Devshirme system that created janissaries . This system took Christian boys, converted them, and turned them into a large fighting force for the Ottoman Empire. An important comparison to make is religious. The Ottoman Empire was mostly Sunni Islam, the Safavids were Shia Islam, and the Mughals were Sikhism, a syncretic religion that blended Hinduism and Islam. These empires commonly fought and competed for power, such as in the Battle of Chaldiran . Chaldiran cemented Ottoman rule over eastern Turkey and Mesopotamia and limited Safavid expansion mostly to Persia. 

⚡ Major powers in the Americas, like the Iroquois, Aztec and Inca, are conquered by Europeans and led to new labor and economic systems

Beginning with Columbus in 1492 and eventually with Cortez and Pizarro, the American indigenous people were conquered by the French, English and the Spanish. Jared Diamond points out in Guns, Germs, and Steel that the Natives lacked the technology and ability to defend from disease to effectively fight back. New labor systems began to be used, some coerced, such as the encomienda and mit’a systems and eventually the use of chattel slavery , with the first slaves landing on the mainland Americas in 1619. African slaves and Native Americans were used primarily for the cultivation of cash crops , which were able to be made the most profitable crops on Earth. These crops, such as sugar led to new emphasis on coerced labor.

⚡ The Atlantic System will see trade increase between the Americas, Europe and Africa and will cause increase in slave trade, especially African corvee slavery

With the introduction of sugar cane to Brazil and the Caribbean, a new trade system emerges. Africans were ruthlessly brought from Africa to be slaves in the Americas where they were used to harvest sugar cane. The sugar, molasses, and rum made from the sugar cane in North America is then sold to Europe for manufacturing. These finished goods, like guns, were traded for slaves with the coastal slave kingdoms in Africa. This system is known as the triangular trade and forms the primary economic systems in this time period.

⚡ The Columbian Exchange will see the movement of food, animals, people, and disease.

The Columbian Exchange was arguably one of the most important events of not just this time period, but in all of world history, and is a term you MUST be familiar with. The Columbian Exchange describes the diffusion of people, food, animals, and notably disease across the Atlantic Ocean both from Europe to the Americas and from the Americas to Europe. Some important things that transferred were smallpox , which killed off some 90% of the Native population, horses , which became a staple in the Americas, cash crops like sugar and tobacco , and then from the Americas, potatoes, which increased the nutrition and lifespan of the average European.

The Columbian Exchange connected the Eastern and Western Hemispheres and created a formally globalized world. The Columbian exchange single handedly caused many of the changes we’ve discussed. 

⚡ Maritime empires emerged as the Portuguese and Dutch created port city empires and the French and British developed large colonies around the world Mercantilism and capitalism emerged as states, businesses, and individuals sought wealth by conquest and new forms of business ventures like joint-stock companies

Unlike the mostly land based empires of the post-classical era, the early modern era was marked by maritime empires, that is, empires that were spread overseas. These typically had imperial metropoles in Europe, such as the British Empire, which had colonies in the Americas and India, the Dutch Empire, that had territory in India and the Philippines, and Portugal and Spain, which had had territories in what is today Latin America.

These empires consolidated power and developed strong economic and financial tools such as joint-stock companies to grow. Companies like the British East India Company and Dutch East India Company became some of the largest companies on earth. Mercantilism became the name of the game economically speaking

⚡ New social structures emerged in Latin America as Spanish, Native Americans, and Africans of pure and mix-blood formed new social castes

The casta system saw Peninsulares (European-born Europeans), Creoles (American-born European descent), Mestizos (Mix European-Native), Mulatoes (Mix European-African), Natives , and Africans hold a strict socio-economic order based on the level of mix-blood. This was the first time in history that a social order was created strictly based off of race. This created a paradigm that continued through nearly all of world history from this point on.

1450-1750 Continuities

🔗Western Europe continued to be largely Christian with powerful monarchies

Though the Roman Catholic Church’s power diminished, Catholic, Orthodox and Protestant Christians continued to be active members of society. In general, Europe will see Christianity rule as the reigning religion, and Catholicism will see power ebb and flow throughout this time. Of course, there were some challenges to religion, especially in the Scientific Revolution and the Enlightenment, but overall religion will still play a HUGE role in the lives of Europeans. 

🔗Land-based empires dominated much of this era from Qing China, Mughal India, Safavid Persia, Ottoman Middle East, and Russia

The new technologies like gunpowder and the unifying force of religion allowed these societies to create empires over vast-areas and for hundreds of years. Though there was conflict, this era can also be measured by the stability of the states. Land empires, despite the growth of maritime empires, continued to have power. The Russian Tsarist Empire grew into the largest land empire in this time period, even going through westernization through Peter the Great . These empires will play a large role in expansion and imperialism in the next time period.

🔗Most societies continue the tradition of patriarchy politically, economically, socially, and culturally.

The Ottoman Janissaries were men, the Qing scholar-gentry were men, and the House of Lords in the English Parliament were men. Though some opportunities existed for women to earn economic and political power, it lacked any sort of consistency.

Period 3 (1750-1900)

1750-1900 changes.

⚡ The Industrial Revolution begins in Western Europe and spreads around the world by 1900

England, with its navigable rivers and wealth of coal deposits, was first to experience the Industrial Revolution. Naturally, Western Europe and the United States also began to thrive because of their connection to the Atlantic trade network. However, empires like Russia, Japan, Ottoman, and Qing were forced to industrialize in order to continue to be politically and economically relevant.

Between the 1700s and mid to late 1800s, the first Industrial Revolution focused on steam power (see Watt’s steam engine from the 1770s) and the transition in economics based around the cottage industry to a new use in factories and mills based off of rivers. Industrialization led to new economic theories such as laissez-faire capitalism and Marxism . Through the first Industrial Revolution, new social classes such as the middle class and industrial working class developed. Governments took specific roles in industrialization as well, such as the Meiji Era changes in Japan and Westernization efforts to avoid imperialism such as the Tanzimat Reforms and Self-Strengthening Movement .

The second Industrial Revolution focused on steel, chemicals, electricity, and precision machinery. Processes like the Bessemer process led to the growth of technology like railroads, mass manufacturing, automobiles, and the assembly line. Social stratification became a significant issue during this time as well.

⚡ The Industrial Revolution causes increased urbanization and diverse economic classes stratification

Cities like Birmingham, England were very attractive to those looking for non-skilled work. As wealth increased its impact on stratification , religion decreased its role. Working class and middle class families living in a city had opportunities for economic advancement, though slow, compared to the rural peasant / farmers. Socioeconomic movements such as Marxism grew, noting social inequities as a result of capitalism and industrialization.

Cities, while growing, were often dangerous and dirty for the lower classes. For example, London was a smog filled, dirty city that was riddled with political corruption and social stratification between the rich and the poor. 

Unionization also became a key staple of urban areas as skilled workers formed unions to protect themselves from unfair policies. For example, the Industrial Workers of the World and American Federation of Labor became large groups that promoted better conditions for workers. They helped to lead to higher wages, better working conditions, and better hours for workers.

⚡ Corvee slavery and serfdom will decrease their role in the Americas and Russia, respectfully

Paid labor was cheaper than maintaining room and board for slaves and serfs and the urban impact of industrialization meant that a constant flow of cheap labor can easily be tapped. As the world transitioned from an economy surrounded by cash crops and mercantilism to a capitalistic industrial world, paid skilled workers became a more effective form of labor as opposed to slaves and serfs who mostly worked in agriculture.

Furthermore, as the Enlightenment spread, slavery and serfdom became seen as immoral in general, with slavery being abolished across most of the world by 1900 and serfdom being abolished from Russia

⚡ Enlightenment thought and fragile social orders will lead to independence movements throughout the Americas and nationalist movements in Europe

Through European colonial powers and merchants, the Enlightenment found their way to the Americas as most of these people became independent by the early 1820s. Revolutions inspired by the Enlightenment became a key sequence of events during the period 1750 - 1900. Specifically, the American Revolution marked the first major revolution that occurred through Enlightenment principles. This quickly led to revolutions in France starting in 1789 and Haiti between 1800 - 1803. Revolutions in Latin America led by Simón Bolívar led to many new independent states in Latin America. Documents such as the Declaration of Independence , Declaration of the Rights of Man and the Citizen , and the Jamaica Letter  

⚡ Nations began expanding more than ever through the process of imperialism

While nations had been expanding from Europe since the Columbian Exchange, industrialization led to a stronger form of territorial imperialism , especially in Africa and Asia. The Berlin Conference of 1884 had Europeans split up Africa into pieces to use for raw materials and access to more markets (M&Ms). Africans were mostly abused for labor, such as in the Belgian Congo , where Africans who did not collect enough rubber had their hands amputated. 

To justify imperialism, nations used philosophies such as social Darwinism and the idea of the White Man’s Burden (see Rudyard Kipling’s “White Man’s Burden”). These racist ideas put down imperial subjects and justified mistreatment as helping them. Political comics from this era, such as the soap advertisement below, portrayed this.

Reactions to imperialism were many, such as the Tanzimat Reforms and Self-Strengthening Movement in the Ottoman Empire and Qing China. Revolts such as the Sepoy Revolt and the Ghost Dance occurred as well, though many times they were violent and unsuccessful. Wars such as the Anglo-Zulu War and the Boer War also occurred.  

1750-1900 Continuities

🔗Monarchies continue to play a role around the world

Though the British and French monarchies saw their power decrease and the Americas tended to stay away from hereditary claims, Russia and Japan continued to solidify their power with strong monarchies. Power structures in the modern era typically were marked by either monarchies or emperors, with constitutional monarchs coming in through revolutions. Monarchies in Europe such as that under Queen Victoria in England and King Leopold II in Belgium played roles in expansion under imperialism. Imperial powers such as the Ottoman Empire and Qing China still had emperors as well. Democracy, however, saw spreads throughout this time period. 

🔗Even with challenges to the norm, most societies continued the tradition of patriarchy politically, economically, socially, and culturally

Women were gaining economic opportunities in many western nations however traditional lacked the ability to vote or hold a high office in the church. Voting rights are still limited to land-owning males in nations that have not seen an increase in the middle-class while women’s suffrage comes in the 20th Century. Feminist movements led by people like Mary Wollstonecraft in the early part of this period and Elizabeth Cady Stanton and Olympe de Gouge by the end leading into the 20th century did occur, though they did not see much significant success until the 20th century.

🔗Raw materials such as spices, cotton, and coal continue to play a large role in domestic, regional, and global trade

As imperialism and industrialization took hold during this time period, raw materials continued to be a significant area of trade and production.

Period 4 (1900-Today)

1900-today changes.

⚡ Rapid advances in science lead to new medicines spreading (polio vaccine), new communications (Internet), new sources of power (nuclear), and new transportation (planes)

Science, now with government and religion, was a driving force of change in human society. The birth control pill allowed family planning, the Internet changed the purchasing process, and the world is more globalized than it has ever been. Medical advancements such as vaccines and by the 1970s the eradication of smallpox led to overall higher global life expectancies. Technology also brought with it new forms of communications like the aforementioned internet, along with telephones , radios , and televisions . Disease played a large role in this time period. Diseases associated with poverty such as malaria and TB persisted, but diseases associated with lifestyles such as Alzheimer’s, diabetes, and heart disease grew.

⚡ Green Revolution and commercial agriculture will allow a population explosion and largely eradicate extreme hunger

The Green Revolution was a process by which new technology was implemented to boost food production. It is marked by a use of biological and organic methods to boost food production such as genetically modified organisms ( GMOs ). GMOs plants, animals or microorganisms whose genetic material has been altered in a way that does not occur naturally. They were used to boost production and help food production rise during this time period. The Green Revolution brought up concerns about global climate change and the relationship between humans and the environment.

⚡ Environmental concerns increase as the developing world industrializes, agri-business use more land, and the global population increases

As scientific technology, especially technology related to farming and agri-business, humans began breaking down the environment around them, leading to things like global climate change and desertification . Deforestation also became a significant concern as humans continued to cut down forests, especially in the Amazon Rainforest, to attain more land for development. Rainforests being removed for grazing, pesticides poisoning crops and bee populations shows the challenges humanity has with the science it created. 

⚡ Globalization seen in various forms like trade (multinational corporations like Coca Cola), epidemics (1918 flu, ebola, AIDS), and immigration of people and ideas

Globalization refers to the technological, political, economic, financial, and cultural exchanges between peoples and nations that have made and continue to make the world a more interconnected and interdependent place. Globalization is an important development that changed essentially everything about the world during this time period. Prior to the 20th century and the global conflict that came with it in the first half, the world, while certainly connected, was still mostly split into individual nations that did not work together on a large scale. As communication increased in the 20th century, globalization became the name of the game. 

Economically, multinational corporations became commonplace, such as Coca Cola, or Nike. Economic and political organizations such as the United Nations , World Bank , IMF , and many others popped up as global entities that helped run the entire world. Free trade deals and international trade agreements such as NAFTA , ASEAN , and the European Union . However, there have been negative effects of globalization, such as a separation of the First World , such as the USA and Western Europe, and the developing Third World , sometimes also described as the Global South , in which there is a larger economic disparity between rich and poor countries. Globalization has also brought with it dissemination of epidemic and pandemic diseases such as the 1918 Influenza Pandemic , the Ebola Epidemic , AIDS Crisis , and most recently, the COVID-19 Pandemic . 

Culturally, new global pop culture grew, such as reggae , bollywood , the olympics , and the World Cup . People conceptualized society and culture in new ways; rights-based discourses challenged old assumptions about race, class, gender, and religion such as global feminist movements and negritude .

Transnational movements also grew, such as the Quebecois movement in Canada, and Pan-Arabism and Pan-Africanism in Africa and the Middle East.

⚡ Older land-based empires like the Ottomans and Qing Dynasty collapsed.

The Ottomans, the once powerful trading center and Islamic hub, fell as it failed to progress successfully. Following World War I, it quickly fell and under Mustafa Kamal Ataturk, it became Turkey . For years prior, the Ottoman Empire had been named the “sick man of Europe” and despite the Tanzimat Reforms helping somewhat, by taking the side of Germany in the first World War, they quickly dissolved into Turkey.

After thousands of years of imperial rule and dynastic succession, the last Chinese dynasty, the Qing fell in 1911 in the Xinhai Revolution to a nationalist uprising led by Sun Yat-Sen and Chiang Kai-shek. Quickly thereafter, the Kuomintang, or Chinese Nationalist Party rose to power and ran the country until 1949, when Mao Zedong helped form the Chinese Communist Party and take over China. 

⚡ Decolonization will see freedom and conflict emerge in nations. 

One of the most important aspects of the 20th century was the process of decolonization , in which countries across the globe broke free of their imperial owners and became independent nations. Most notably, decolonization in the 20th century took place in Africa, South Asia, and Southeast Asia.

In Africa, decolonization was widespread. In North Africa, such as in Algeria , decolonization was met with violence and the death of 140,000 Algerian soldiers. Elsewhere, decolonization was negotiated, such as protests against apartheid until South Africa’s independence in 1994 under Nelson Mandela . Similarly, after World War II, French West Africa split into many nations such as Guinea, Senegal, Côte D’Ivoire, and Niger.

India is an important example of decolonization that you must know. Led by Mohandas Gandhi and Muhammad Ali Jinnah , India gained its independence through civil disobedience such as the Salt March. However, after succeeding in their goal, Jinnah and the Muslim League , split off into the state of Pakistan , in which heavy border disputes ensued.

In Southeast Asia, the most significant decolonized states were Vietnam and Cambodia . In Vietnam, Ho Chi Minh led a violent revolution to overthrow the French, with the French finally losing at Dien Bien Phu. Vietnam quickly became communist and split into South Vietnam and North Vietnam, and the Vietnam War soon followed as part of the Cold War. Cambodia similarly formed a Marxist state and under Pol Pot , the Cambodian Genocide took out any signs of intellectualism.

⚡ Global conflicts over land and political ideologies increase in the first half of the 20th Century

The first major global conflict to occur in this time period was the First World War . After the killing of the Archduke Franz Ferdinand, the Triple Alliance and Triple Entente quickly caused the war to escalate from a regional crisis to a world wide war. WWI had MANIA causes: (List Mania Causes). The First World War ended in November of 1918 after the Third Battle of Picardy and the signing of the Treaty of Versailles , which blamed Germany and charged them massive war reparations. World War I also saw new forms of war such as trench warfare and the use of chlorine gas by the Germans.

Following World War I, the interwar years saw massive debt and inflation on the German side caused complete economic collapse. This, compounded by the Great Depression in 1929, contributed to the rise of Adolf Hitler in 1933 and the German invasion of Poland in 1939 and the start of World War II. World War II was another major global conflict that involved the Allied Powers and Axis Powers and ended with another German defeat in 1941 after the Battle of the Bulge.

World War II similarly saw the beginning of genocides , the systematic murder of a race of people. Global genocides were relatively common during the 20th century, beginning with the Armenian Genocide during World War I 

1900-Today Continuities

🔗The process of Westernization continues outside Europe and the United States to Japan, South Korea, Russia, India and beyond

Bollywood emerges as a center for movie making while pop-stars from South Korea thrive in the global market, and cities like Tokyo, Japan look just like New York City with their lights and sounds.

🔗Economic globalization that started with the Silk Road continues on land, sea, and air

Cotton from Georgia is shipped to Bangladesh to be made into a shirt which is then shipped to Hondars to be printed on and then back to the US for retail sail. Hands from three continents played a role in a simple shirt finding it cheaper to move the materials around than find one location to produce the whole item.

Patriarchy and racist beliefs, despite seeing vast improvements, still exist

Full Course Review for AP World History

Watch the AP World History 5-Hour Cram Finale for a comprehensive last minute cram session covering the entire WHAP curriculum including every unit, every time period, and every type of question you will come against during the exam.

Here is a breakdown of the review schedule and timeline:

• 30 min - Overview (Sorting by theme, region, and time periods)
• 1 hour - 1200-1450 CE
• 1 hour - 1450-1750 CE
• 1 hour - 1750-1900 CE
• 1 hour - 1900-Present
• 30 min - Final thoughts (Time management, strategies, and pep talk)

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What Does It Mean to Think Historically?

Thomas Andrews and Flannery Burke | Jan 1, 2007

Introduction

When we started working on Teachers for a New Era, a Carnegie-sponsored initiative designed to strengthen teacher training, we thought we knew a thing or two about our discipline. As we began reading such works as Sam Wineburg's Historical Thinking and Other Unnatural Acts , however, we encountered an unexpected challenge. 1 If our understandings of the past constituted a sort of craft knowledge, how could we distill and communicate habits of mind we and our colleagues had developed through years of apprenticeship, guild membership, and daily practice to university students so that they, in turn, could impart these habits in K–12 classrooms?

In response, we developed an approach we call the "five C's of historical thinking." The concepts of change over time, causality, context, complexity, and contingency, we believe, together describe the shared foundations of our discipline. They stand at the heart of the questions historians seek to answer, the arguments we make, and the debates in which we engage. These ideas are hardly new to professional historians. But that is precisely their value: They make our implicit ways of thought explicit to the students and teachers whom we train. The five C's do not encompass the universe of historical thinking, yet they do provide a remarkably useful tool for helping students at practically any level learn how to formulate and support arguments based on primary sources, as well as to understand and challenge historical interpretations related in secondary sources. In this article, we define the five C's, explain how each concept helps us to understand the past, and provide some brief examples of how we have employed the five C's when teaching teachers. Our approach is necessarily broad and basic, characteristics well suited for a foundation upon which we invite our colleagues from kindergartens to research universities to build.

Change over Time

The idea of change over time is perhaps the easiest of the C's to grasp. Students readily acknowledge that we employ and struggle with technologies unavailable to our forebears, that we live by different laws, and that we enjoy different cultural pursuits. Moreover, students also note that some aspects of life remain the same across time. Many Europeans celebrate many of the same holidays that they did three or four hundred years ago, for instance, often using the same rituals and words to mark a day's significance. Continuity thus comprises an integral part of the idea of change over time.

Students often find the concept of change over time elementary. Even individuals who claim to despise history can remember a few dates and explain that some preceded or followed others. At any educational level, timelines can teach change over time as well as the selective process that leads people to pay attention to some events while ignoring others. In our U.S. survey class, we often ask students to interview family and friends and write a paper explaining how their family's history has intersected with major events and trends that we are studying. By discovering their own family's past, students often see how individuals can make a difference and how personal history changes over time along with major events.

As historians of the American West and environmental historians, we often turn to maps to teach change over time. The same space represented in different ways as political power, economic structures, and cultural influences shift can often put in shocking relief the differences that time makes. The work of repeat photographers such as Mark Klett offers another compelling tool for teaching change over time. Such photographers begin with a historic landscape photograph, then take pains to re-take the shot from the same site, at the same angle, using similar equipment, and even under analogous conditions. 2 While suburbs and industry have overrun many western locales, students are often surprised to see that some places have become more desolate and others have hardly changed at all. The exercise engages students with a non-written primary source, photographs, and demands that they reassess their expectations regarding how time changes.

Some things change, others stay the same—not a very interesting story but reason for concern since history, as the best teachers will tell you, is about telling stories. Good story telling, we contend, builds upon an understanding of context. Given young people's fascination with narratives and their enthusiasm for imaginative play, pupils (particularly elementary school students) often find context the most engaging element of historical thinking. As students mature, of course, they recognize that the past is not just a playful alternate universe. Working with primary sources, they discover that the past makes more sense when they set it within two frameworks. In our teaching, we liken the first to the floating words that roll across the screen at the beginning of every Star Wars film. This kind of context sets the stage; the second helps us to interpret evidence concerning the action that ensues. Texts, events, individual lives, collective struggles—all develop within a tightly interwoven world.

Historians who excel at the art of storytelling often rely heavily upon context. Jonathan Spence's Death of Woman Wang , for example, skillfully recreates 17th-century China by following the trail of a sparsely documented murder. To solve the mystery, students must understand the time and place in which it occurred. Laurel Thatcher Ulrich brings colonial New England to life by concentrating on the details of textile production and basket making in Age of Homespun . College courses regularly use the work of both authors because they not only spark student interest, but also hone students' ability to describe the past and identify distinctive elements of different eras. 3

Imaginative play is what makes context, arguably the easiest, yet also, paradoxically, the most difficult of the five C's to teach. Elementary school assignments that require students to research and wear medieval European clothes or build a California mission from sugar cubes both strive to teach context. The problem with such assignments is that they often blur the lines between reality and make-believe. The picturesque often trumps more banal or more disturbing truths. Young children may never be able to get all the facts straight. As one elementary school teacher once reminded us, "We teach kids who still believe in Santa Claus." Nonetheless, elementary school teachers can be cautious in their re-creations, and, most of all, they can be comfortable telling students when they don't know a given fact or when more research is necessary. That an idea might require more thought or more research is a valuable lesson at any age. The desire to recreate a world sometimes drives students to dig more deeply into their books, a reaction few teachers lament.

In our own classes, we have taught context using an assignment that we call "Fact, Fiction, or Creative Memory." In this exercise, students wrestle with a given source and determine whether it is primarily a work of history, fiction, or memory. We have asked students to bring in a present-day representation of 1950s life and explain what it teaches people today about life in 1950s America. Then, we have asked the class to discuss if the representation is a historically fair depiction of the era. We have also assigned textbook passages and Don DeLillo's Pafko at the Wall , then asked students to compare them to decide which offers stronger insights into the character of Cold War America. 4 Each of these assignments addresses context, because each asks students to think about the distinctions between representations of the past and the critical thinking about the past that is history. Moreoever, each asks students to weave together a variety of sources and assess the reliability of each before incorporating them into a whole.

Historians use context, change over time, and causality to form arguments explaining past change. While scientists can devise experiments to test theories and yield data, historians cannot alter past conditions to produce new information. Rather, they must base their arguments upon the interpretation of partial primary sources that frequently offer multiple explanations for a single event. Historians have long argued over the causes of the Protestant Reformation or World War I, for example, without achieving consensus. Such uncertainty troubles some students, but history classrooms are at their most dynamic when teachers encourage pupils to evaluate the contributions of multiple factors in shaping past events, as well as to formulate arguments asserting the primacy of some causes over others.

To teach causality, we have turned to the stand-by activities of the history classroom: debates and role-playing. After arming students with primary sources, we ask them to argue whether monetary or fiscal policy played a greater role in causing the Great Depression. After giving students descriptions drawn from primary sources of immigrant families in Los Angeles, we have asked students to take on the role of various family members and explain their reasons for immigrating and their reasons for settling in particular neighborhoods. Neither exercise is especially novel, but both fulfill a central goal of studying history: to develop persuasive explanations of historical events and processes based on logical interpretations of evidence.

Contingency

Contingency may, in fact, be the most difficult of the C's. To argue that history is contingent is to claim that every historical outcome depends upon a number of prior conditions; that each of these prior conditions depends, in turn, upon still other conditions; and so on. The core insight of contingency is that the world is a magnificently interconnected place. Change a single prior condition, and any historical outcome could have turned out differently. Lee could have won at Gettysburg, Gore might have won in Florida, China might have inaugurated the world's first industrial revolution. Contingency can be an unsettling idea—so much so that people in the past have often tried to mask it with myths of national and racial destiny. The Pilgrim William Bradford, for instance, interpreted the decimation of New England's native peoples not as a consequence of smallpox, but as a literal godsend. 5 Two centuries later, American ideologues chose to rationalize their unlikely fortunes—from the purchase of Louisiana to the discovery of gold in California—as their nation's "Manifest Destiny." Historians, unlike Bradford and the apologists of westward expansion, look at the same outcomes differently. They see not divine fate, but a series of contingent results possessing other possibilities.

Contingency demands that students think deeply about past, present, and future. It offers a powerful corrective to teleology, the fallacy that events pursue a straight-arrow course to a pre-determined outcome, since people in the past had no way of anticipating our present world. Contingency also reminds us that individuals shape the course of human events. What if Karl Marx had decided to elude Prussian censors by emigrating to the United States instead of France, where he met Frederick Engels? To assert that the past is contingent is to impress upon students the notion that the future is up for grabs, and that they bear some responsibility for shaping the course of future history.

Contingency can be a difficult concept to present abstractly, but it suffuses the stories historians tend to tell about individual lives. Futurology, however, might offer an even stronger tool for imparting contingency than biography. Mechanistic views of history as the inevitable march toward the present tend to collapse once students see how different their world is from any predicted in the past.

Moral, epistemological, and causal complexity distinguish historical thinking from the conception of "history" held by many non-historians. 6 Re-enacting battles and remembering names and dates require effort but not necessarily analytical rigor. Making sense of a messy world that we cannot know directly, in contrast, is more confounding but also more rewarding.

Chronicles distill intricate historical processes into a mere catalogue, while nostalgia conjures an uncomplicated golden age that saves us the trouble of having to think about the past. Our own need for order can obscure our understanding of how past worlds functioned and blind us to the ways in which myths of rosy pasts do political and cultural work in the present. Reveling in complexity rather than shying away from it, historians seek to dispel the power of chronicle, nostalgia, and other traps that obscure our ability to understand the past on its own terms.

One of the most successful exercises we have developed for conveying complexity in all of these dimensions is a mock debate on Cherokee Removal. Two features of the exercise account for the richness and depth of understanding that it imparts on students. First, the debate involves multiple parties; the Treaty and Anti-Treaty Parties, Cherokee women, John Marshall, Andrew Jackson, northern missionaries, the State of Georgia, and white settlers each offer a different perspective on the issue. Second, students develop their understanding of their respective positions using the primary sources collected in Cherokee Removal: A Brief History with Documents by Theda Perdue and Michael Green. 7 While it can be difficult to assess what students learn from such exercises, we have noted anecdotally that, following the exercise, students seem much less comfortable referring to "American" or "Indian" positions as monolithic identities.

Our experiments with the five C's have confronted us with several challenges. These concepts offer a fluid tool for engaging historical thought at multiple levels, but they can easily degenerate into a checklist. Students who favor memorization over analysis seem inclined to recite the C's without necessarily understanding them. Moreover, as habits of mind, the five C's develop only with practice. Though primary and secondary schools increasingly emphasize some aspects of these themes, particularly the use of primary sources as evidence, more attention to the five C's with appropriate variations over the course of K–12 education would help future citizens not only to care about history, but also to contemplate it. It is our hope that this might help students to see the past not simply as prelude to our present, nor a list of facts to memorize, a cast of heroes and villains to cheer and boo, nor as an itinerary of places to tour, but rather as an ideal field for thinking long and hard about important questions.

—Flannery Burke and Thomas Andrews are both assistant professors of history and Teachers for a New Era faculty members at California State University at Northridge. Burke is working on a book for the University Press of Kansas tentatively entitled Longing and Belonging: Mabel Dodge Luhan and Greenwich Village's Avant-Garde in Taos . Andrews is completing a manuscript for Harvard University Press, tentatively entitled Ludlow: The Nature of Industrial Struggle in the Colorado Coalfields .

1. Sam Wineburg, Historical Thinking and Other Unnatural Acts: Charting the Future of Teaching the Past (Philadelphia: Temple University Press, 2001).

2. Mark Klett, Kyle Bajakian, William L. Fox, Michael Marshall, Toshi Ueshina, and Byron G. Wolfe, Third Views, Second Sights: A Rephotographic Survey of the American West (Santa Fe: Museum of New Mexico Press, 2004).

3. Jonathan D. Spence, Death of Woman Wang (New York: Viking, 1978); Laurel Thatcher Ulrich, The Age of Homespun: Objects and Stories in the Creation of an American Myth (New York: Knopf, 2001).

4. Don DeLillo, Pafko at the Wall: A Novella (New York: Scribner's, 2001).

5. William Bradford, Of Plymouth Plantation , ed. Samuel Eliot Morison (New York: Random House, 1952).

6. Roy Rosenzweig and David Thelen, The Presence of the Past: Popular Uses of History in American Life (New York: Columbia University Press, 1998).

7. Theda Perdue and Michael D. Green, The Cherokee Removal: A Brief History with Documents 2nd ed. (Boston and New York: Bedford/St. Martin's, 2005).

Tags: K12 Certification & Curricula Teaching Resources and Strategies

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Change Over Time Essay

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How to Write a Change-Over-Time Essay

Janie sullivan.

Change-over-time essays deal with global issues and how they either change or effect change on the environment. Timelines are important in change-over-time essays. The writer will typically synthesize a large portion of a timeline, using significant events to anchor the content in the essay. A change-over-time essay is not a research paper; it is a paper that supports a thesis with sound analysis rather than a rehash of an article in an encyclopedia.

Explore this article

• Steps in Essay Writing
• Find a topic to address
• Create a thesis statement

1 Steps in Essay Writing

2 find a topic to address.

Find a topic to address. Change-over-time essays follow the same basic rules as writing comparative essays. They deal with change that has occurred in an area such as technology, trade, culture, migrations or religious influence. Choose a period of time that best illustrates the change that has occurred.

3 Create a thesis statement

Create a thesis statement. This should include the date range or time period that will be covered, the region or country affected by the change, and what the actual change was.

Support the thesis statement by the content of the essay. Do the research and use more than just obvious similarities and differences exhibited during the period of time chosen.

Use the acronym "SPRITE" when reviewing the essay to be sure all aspects of the thesis have been covered. "SPRITE" stands for "Social, Political, Religious, Intellectual, Technological, Economic."

Write the conclusion of the essay in much the same way as a conclusion for a comparison essay is written. Include the premise stated in the thesis and summarize what the content revealed about the statement.

• Be sure to cite any sources appropriately.
• 1 Change Over Time: The Silk Road

About the Author

Janie Sullivan, a freelance writer living in Apache Junction, Arizona, has had articles published in Small Business Start-Ups and The Adjunct Advocate magazines. She earned a Bachelor of Arts in journalism from the University of Montana and both a Master of Business Administration and a Master of Arts in Education from the University of Phoenix.

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Change And Continuity Over Time Essay Examples

Type of paper: Essay

Topic: War , Africa , Economics , Time , Europe , Colonization , World , Colony

Published: 02/06/2020

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World War I resulted in considerably varying consequences in different African territories, depending on the extent to which they had been involved in the war. No doubt, new doors were opened up after the war for many Africans, especially the educated elite groups. The war also boosted nationalist activity in many parts of Africa and a more critical approach was being taken towards colonial matters. With the end of the war, Africans also stopped trying to regain the sovereignty of polities that had been lost after the colonization of Africa. However, at the same time the demands of Africans to take part in the process of government of the new politics the Europeans had imposed on them. These demands served as an inspiration for the Fourteen Points of President Woodrow Wilson. The demands for self determination began rising throughout Africa, especially in Egypt and Tunisia. Although the Fourteen Points of Wilson did not serve as an inspiration for the demands for immediate independence in Africa, however, West African nationalists were encouraged to be hopeful in influencing the Versailles Peace Conference and demanding a greater involvement in their own affairs. The Fourteen Points of Wilson resulted in a decisive change in Sudanese nationalism, giving rise to a new generation of young African men with informed attitudes, who had studied in government schools and possessed some modern western skills. Thus, the First World War also marked a decisive but perhaps not a dramatic change in African history, whereas the elimination of a Germany as a colonial power as a result of the Treaty of Versailles, and the its replacement by Britain and France can be viewed as a continuation of European influence. The end of the First World War not only ended the partition of Africa but also temporarily halted African efforts to regain independence. The 1920s marked the formulation of and application of colonial policies regarding labor, land and economic development. By the 1920s, Africans began influencing the terms of their involvement in the African economy resulting in a new phase in economic relations. During this period, Africans continued to participate and play their role in the colonial economy by working on the plantations in Central and East Africa and Congo, and in the mines in South Africa. Economic opposition during t his time period was not well organized in most cases. By 1920, it was obvious that the colonial government was condoning racial segregation everywhere throughout the African continent. Mass protests against colonial policies of the 1920s were scarce, apart from the Aba Women’s War in 1929 by southeastern Nigerian women. The end of the 1920s also marked the arrival of the economic depression that resulted in equal suffering of Africans and the owners of the markets. During the Second World War, Africa was forced to compensate for the shortage of raw materials in the Far East and this would prove to be very beneficial for the independence of African nations. As a result, the transportation of raw materials to Europe and so local industries had to be created in Africa. New towns were created as a result of the local industrialization and size of existing towns doubled. The growth of industry and urban community also marked the growth of trade unions and literacy also increased, all of which contributed to the ultimate independence of the African continent. European economies had been utterly devastated for a second time by the end of the Second World War in 1945 and with the European nationals financially ruined; they could no longer afford to keep the African colonies. Moreover, they realized that they could also no longer justify keeping African societies under colonial rule.

Works Cited

Birmingham, David. The Decolonization Of Africa. Routledge, 1997. Print. Kevin, Shillington. A History Of South Africa. 2nd ed. New York: Palgrave MacMiller, 2005. Print.

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Scientific Consensus

It’s important to remember that scientists always focus on the evidence, not on opinions. Scientific evidence continues to show that human activities ( primarily the human burning of fossil fuels ) have warmed Earth’s surface and its ocean basins, which in turn have continued to impact Earth’s climate . This is based on over a century of scientific evidence forming the structural backbone of today's civilization.

NASA Global Climate Change presents the state of scientific knowledge about climate change while highlighting the role NASA plays in better understanding our home planet. This effort includes citing multiple peer-reviewed studies from research groups across the world, 1 illustrating the accuracy and consensus of research results (in this case, the scientific consensus on climate change) consistent with NASA’s scientific research portfolio.

With that said, multiple studies published in peer-reviewed scientific journals 1 show that climate-warming trends over the past century are extremely likely due to human activities. In addition, most of the leading scientific organizations worldwide have issued public statements endorsing this position. The following is a partial list of these organizations, along with links to their published statements and a selection of related resources.

American Scientific Societies

Statement on climate change from 18 scientific associations.

"Observations throughout the world make it clear that climate change is occurring, and rigorous scientific research demonstrates that the greenhouse gases emitted by human activities are the primary driver." (2009) 2

American Association for the Advancement of Science

"Based on well-established evidence, about 97% of climate scientists have concluded that human-caused climate change is happening." (2014) 3

American Chemical Society

"The Earth’s climate is changing in response to increasing concentrations of greenhouse gases (GHGs) and particulate matter in the atmosphere, largely as the result of human activities." (2016-2019) 4

American Geophysical Union

"Based on extensive scientific evidence, it is extremely likely that human activities, especially emissions of greenhouse gases, are the dominant cause of the observed warming since the mid-20th century. There is no alterative explanation supported by convincing evidence." (2019) 5

American Medical Association

"Our AMA ... supports the findings of the Intergovernmental Panel on Climate Change’s fourth assessment report and concurs with the scientific consensus that the Earth is undergoing adverse global climate change and that anthropogenic contributions are significant." (2019) 6

American Meteorological Society

"Research has found a human influence on the climate of the past several decades ... The IPCC (2013), USGCRP (2017), and USGCRP (2018) indicate that it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-twentieth century." (2019) 7

American Physical Society

"Earth's changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe. While natural sources of climate variability are significant, multiple lines of evidence indicate that human influences have had an increasingly dominant effect on global climate warming observed since the mid-twentieth century." (2015) 8

The Geological Society of America

"The Geological Society of America (GSA) concurs with assessments by the National Academies of Science (2005), the National Research Council (2011), the Intergovernmental Panel on Climate Change (IPCC, 2013) and the U.S. Global Change Research Program (Melillo et al., 2014) that global climate has warmed in response to increasing concentrations of carbon dioxide (CO2) and other greenhouse gases ... Human activities (mainly greenhouse-gas emissions) are the dominant cause of the rapid warming since the middle 1900s (IPCC, 2013)." (2015) 9

Science Academies

International academies: joint statement.

"Climate change is real. There will always be uncertainty in understanding a system as complex as the world’s climate. However there is now strong evidence that significant global warming is occurring. The evidence comes from direct measurements of rising surface air temperatures and subsurface ocean temperatures and from phenomena such as increases in average global sea levels, retreating glaciers, and changes to many physical and biological systems. It is likely that most of the warming in recent decades can be attributed to human activities (IPCC 2001)." (2005, 11 international science academies) 1 0

U.S. National Academy of Sciences

"Scientists have known for some time, from multiple lines of evidence, that humans are changing Earth’s climate, primarily through greenhouse gas emissions." 1 1

U.S. Government Agencies

U.s. global change research program.

"Earth’s climate is now changing faster than at any point in the history of modern civilization, primarily as a result of human activities." (2018, 13 U.S. government departments and agencies) 12

Intergovernmental Bodies

Intergovernmental panel on climate change.

“It is unequivocal that the increase of CO 2 , methane, and nitrous oxide in the atmosphere over the industrial era is the result of human activities and that human influence is the principal driver of many changes observed across the atmosphere, ocean, cryosphere, and biosphere. “Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact.” 1 3-17

Other Resources

List of worldwide scientific organizations.

The following page lists the nearly 200 worldwide scientific organizations that hold the position that climate change has been caused by human action. http://www.opr.ca.gov/facts/list-of-scientific-organizations.html

U.S. Agencies

The following page contains information on what federal agencies are doing to adapt to climate change. https://www.c2es.org/site/assets/uploads/2012/02/climate-change-adaptation-what-federal-agencies-are-doing.pdf

Technically, a “consensus” is a general agreement of opinion, but the scientific method steers us away from this to an objective framework. In science, facts or observations are explained by a hypothesis (a statement of a possible explanation for some natural phenomenon), which can then be tested and retested until it is refuted (or disproved).

As scientists gather more observations, they will build off one explanation and add details to complete the picture. Eventually, a group of hypotheses might be integrated and generalized into a scientific theory, a scientifically acceptable general principle or body of principles offered to explain phenomena.

1. K. Myers, et al, "Consensus revisited: quantifying scientific agreement on climate change and climate expertise among Earth scientists 10 years later", Environmental Research Letters Vol.16 No. 10, 104030 (20 October 2021); DOI:10.1088/1748-9326/ac2774 M. Lynas, et al, "Greater than 99% consensus on human caused climate change in the peer-reviewed scientific literature", Environmental Research Letters Vol.16 No. 11, 114005 (19 October 2021); DOI:10.1088/1748-9326/ac2966 J. Cook et al., "Consensus on consensus: a synthesis of consensus estimates on human-caused global warming", Environmental Research Letters Vol. 11 No. 4, (13 April 2016); DOI:10.1088/1748-9326/11/4/048002 J. Cook et al., "Quantifying the consensus on anthropogenic global warming in the scientific literature", Environmental Research Letters Vol. 8 No. 2, (15 May 2013); DOI:10.1088/1748-9326/8/2/024024 W. R. L. Anderegg, “Expert Credibility in Climate Change”, Proceedings of the National Academy of Sciences Vol. 107 No. 27, 12107-12109 (21 June 2010); DOI: 10.1073/pnas.1003187107 P. T. Doran & M. K. Zimmerman, "Examining the Scientific Consensus on Climate Change", Eos Transactions American Geophysical Union Vol. 90 Issue 3 (2009), 22; DOI: 10.1029/2009EO030002 N. Oreskes, “Beyond the Ivory Tower: The Scientific Consensus on Climate Change”, Science Vol. 306 no. 5702, p. 1686 (3 December 2004); DOI: 10.1126/science.1103618

2. Statement on climate change from 18 scientific associations (2009)

3. AAAS Board Statement on Climate Change (2014)

4. ACS Public Policy Statement: Climate Change (2016-2019)

5. Society Must Address the Growing Climate Crisis Now (2019)

6. Global Climate Change and Human Health (2019)

7. Climate Change: An Information Statement of the American Meteorological Society (2019)

8. American Physical Society (2021)

9. GSA Position Statement on Climate Change (2015)

10. Joint science academies' statement: Global response to climate change (2005)

11. Climate at the National Academies

12. Fourth National Climate Assessment: Volume II (2018)

13. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1.1 (2014)

14. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1 (2014)

15. IPCC Sixth Assessment Report, Working Group 1 (2021)

16. IPCC Sixth Assessment Report, Working Group 2 (2022)

17. IPCC Sixth Assessment Report, Working Group 3 (2022)

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Carbon dioxide (CO 2 ) is an important heat-trapping gas, also known as a greenhouse gas, that comes from the extraction and burning of fossil fuels (such as coal, oil, and natural gas), from wildfires, and natural processes like volcanic eruptions. The first graph shows atmospheric CO 2 levels measured by NOAA at Mauna Loa Observatory, Hawaii, since 1958. The second graph shows CO 2 levels during Earth’s last three glacial cycles, as captured by air bubbles trapped in ice sheets and glaciers.

Since the onset of industrial times in the 18th century, human activities have raised atmospheric CO 2 by 50% – meaning the amount of CO 2 is now 150% of its value in 1750. This human-induced rise is greater than the natural increase observed at the end of the last ice age 20,000 years ago.

The animated map shows how the historical changes in global carbon dioxide over time. Note the colors change as the amount of CO 2 rises from 365 parts per million (ppm) in 2002 to over 420 ppm currently. It's important to understand that “parts per million” refers to the number of carbon dioxide molecules per million molecules of dry air. These measurements are from the mid-troposphere, the layer of Earth's atmosphere that is 8 to 12 kilometers (about 5 to 7 miles) above the ground. This data provides insights into the significant rise in atmospheric CO2 concentrations, highlighting the impact of human activities on Earth's climate.

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See the cycle in action in the visualization Watching Earth Breathe: The Seasonal Vegetation Cycle and Atmospheric Carbon Dioxide .

This boom-and-bust cycle of plant growth gives the graph of CO 2 a sawtooth pattern of ups and downs from year to year. At a larger scale, the upward climb of the trend line over the decades is caused by CO 2 emissions, primarily from burning fossil fuels. Thus, the data illustrate both natural factors and human additions of CO 2 .

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• Published: 20 March 2024

Persistent interaction patterns across social media platforms and over time

• Michele Avalle   ORCID: orcid.org/0009-0007-4934-2326 1   na1 ,
• Niccolò Di Marco 1   na1 ,
• Gabriele Etta 1   na1 ,
• Emanuele Sangiorgio   ORCID: orcid.org/0009-0003-1024-3735 2 ,
• Shayan Alipour 1 ,
• Anita Bonetti 3 ,
• Lorenzo Alvisi 1 ,
• Antonio Scala 4 ,
• Andrea Baronchelli 5 , 6 ,
• Matteo Cinelli   ORCID: orcid.org/0000-0003-3899-4592 1 &
• Walter Quattrociocchi   ORCID: orcid.org/0000-0002-4374-9324 1  

Nature ( 2024 ) Cite this article

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• Social sciences

Growing concern surrounds the impact of social media platforms on public discourse 1 , 2 , 3 , 4 and their influence on social dynamics 5 , 6 , 7 , 8 , 9 , especially in the context of toxicity 10 , 11 , 12 . Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years—from Usenet to contemporary social media—our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.

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The advent and proliferation of social media platforms have not only transformed the landscape of online participation 2 but have also become integral to our daily lives, serving as primary sources for information, entertainment and personal communication 13 , 14 . Although these platforms offer unprecedented connectivity and information exchange opportunities, they also present challenges by entangling their business models with complex social dynamics, raising substantial concerns about their broader impact on society. Previous research has extensively addressed issues such as polarization, misinformation and antisocial behaviours in online spaces 5 , 7 , 12 , 15 , 16 , 17 , revealing the multifaceted nature of social media’s influence on public discourse. However, a considerable challenge in understanding how these platforms might influence inherent human behaviours lies in the general lack of accessible data 18 . Even when researchers obtain data through special agreements with companies like Meta, it may not be enough to clearly distinguish between inherent human behaviours and the effects of the platform’s design 3 , 4 , 8 , 9 . This difficulty arises because the data, deeply embedded in platform interactions, complicate separating intrinsic human behaviour from the influences exerted by the platform’s design and algorithms.

Here we address this challenge by focusing on toxicity, one of the most prominent aspects of concern in online conversations. We use a comparative analysis to uncover consistent patterns across diverse social media platforms and timeframes, aiming to shed light on toxicity dynamics across various digital environments. In particular, our goal is to gain insights into inherently invariant human patterns of online conversations.

The lack of non-verbal cues and physical presence on the web can contribute to increased incivility in online discussions compared with face-to-face interactions 19 . This trend is especially pronounced in online arenas such as newspaper comment sections and political discussions, where exchanges may degenerate into offensive comments or mockery, undermining the potential for productive and democratic debate 20 , 21 . When exposed to such uncivil language, users are more likely to interpret these messages as hostile, influencing their judgement and leading them to form opinions based on their beliefs rather than the information presented and may foster polarized perspectives, especially among groups with differing values 22 . Indeed, there is a natural tendency for online users to seek out and align with information that echoes their pre-existing beliefs, often ignoring contrasting views 6 , 23 . This behaviour may result in the creation of echo chambers, in which like-minded individuals congregate and mutually reinforce shared narratives 5 , 24 , 25 . These echo chambers, along with increased polarization, vary in their prevalence and intensity across different social media platforms 1 , suggesting that the design and algorithms of these platforms, intended to maximize user engagement, can substantially shape online social dynamics. This focus on engagement can inadvertently highlight certain behaviours, making it challenging to differentiate between organic user interaction and the influence of the platform’s design. A substantial portion of current research is devoted to examining harmful language on social media and its wider effects, online and offline 10 , 26 . This examination is crucial, as it reveals how social media may reflect and amplify societal issues, including the deterioration of public discourse. The growing interest in analysing online toxicity through massive data analysis coincides with advancements in machine learning capable of detecting toxic language 27 . Although numerous studies have focused on online toxicity, most concentrate on specific platforms and topics 28 , 29 . Broader, multiplatform studies are still limited in scale and reach 12 , 30 . Research fragmentation complicates understanding whether perceptions about online toxicity are accurate or misconceptions 31 . Key questions include whether online discussions are inherently toxic and how toxic and non-toxic conversations differ. Clarifying these dynamics and how they have evolved over time is crucial for developing effective strategies and policies to mitigate online toxicity.

Our study involves a comparative analysis of online conversations, focusing on three dimensions: time, platform and topic. We examine conversations from eight different platforms, totalling about 500 million comments. For our analysis, we adopt the toxicity definition provided by the Perspective API, a state-of-the-art classifier for the automatic detection of toxic speech. This API considers toxicity as “a rude, disrespectful or unreasonable comment likely to make someone leave a discussion”. We further validate this definition by confirming its consistency with outcomes from other detection tools, ensuring the reliability and comparability of our results. The concept of toxicity in online discourse varies widely in the literature, reflecting its complexity, as seen in various studies 32 , 33 , 34 . The efficacy and constraints of current machine-learning-based automated toxicity detection systems have recently been debated 11 , 35 . Despite these discussions, automated systems are still the most practical means for large-scale analyses.

Here we analyse online conversations, challenging common assumptions about their dynamics. Our findings reveal consistent patterns across various platforms and different times, such as the heavy-tailed nature of engagement dynamics, a decrease in user participation and an increase in toxic speech in lengthier conversations. Our analysis indicates that, although toxicity and user participation in debates are independent variables, the diversity of opinions and sentiments among users may have a substantial role in escalating conversation toxicity.

To obtain a comprehensive picture of online social media conversations, we analysed a dataset of about 500 million comments from Facebook, Gab, Reddit, Telegram, Twitter, Usenet, Voat and YouTube, covering diverse topics and spanning over three decades (a dataset breakdown is shown in Table 1 and Supplementary Table 1 ; for details regarding the data collection, see the ‘Data collection’ section of the Methods ).

Our analysis aims to comprehensively compare the dynamics of diverse social media accounting for human behaviours and how they evolved. In particular, we first characterize conversations at a macroscopic level by means of their engagement and participation, and we then analyse the toxicity of conversations both after and during their unfolding. We conclude the paper by examining potential drivers for the emergence of toxic speech.

Conversations on different platforms

This section provides an overview of online conversations by considering user activity and thread size metrics. We define a conversation (or a thread) as a sequence of comments that follow chronologically from an initial post. In Fig. 1a and Extended Data Fig. 1 , we observe that, across all platforms, both user activity (defined as the number of comments posted by the user) and thread length (defined as the number of comments in a thread) exhibit heavy-tailed distributions. The summary statistics about these distributions are reported in Supplementary Tables 1 and 2 .

a , The distributions of user activity in terms of comments posted for each platform and each topic. b , The mean user participation as conversations evolve. For each dataset, participation is computed for the threads belonging to the size interval [0.7–1] (Supplementary Table 2 ). Trends are reported with their 95% confidence intervals. The x axis represents the normalized position of comment intervals in the threads.

Consistent with previous studies 36 , 37 our analysis shows that the macroscopic patterns of online conversations, such as the distribution of users/threads activity and lifetime, are consistent across all datasets and topics (Supplementary Tables 1 – 4 ). This observation holds regardless of the specific features of the diverse platforms, such as recommendation algorithms and moderation policies (described in the ‘Content moderation policies’ of the Methods ), as well as other factors, including the user base and the conversation topics. We extend our analysis by examining another aspect of user activity within conversations across all platforms. To do this, we introduce a metric for the participation of users as a thread evolves. In this analysis, threads are filtered to ensure sufficient length as explained in the ‘Logarithmic binning and conversation size’ section of the Methods .

The participation metric, defined over different conversation intervals (that is, 0–5% of the thread arranged in chronological order, 5–10%, and so on), is the ratio of the number of unique users to the number of comments in the interval. Considering a fixed number of comments c , smaller values of participation indicate that fewer unique users are producing c comments in a segment of the conversation. In turn, a value of participation equal to 1 means that each user is producing one of the c comments, therefore obtaining the maximal homogeneity of user participation. Our findings show that, across all datasets, the participation of users in the evolution of conversations, averaged over almost all considered threads, is decreasing, as indicated by the results of Mann–Kendall test—a nonparametric test assessing the presence of a monotonic upward or downward tendency—shown in Extended Data Table 1 . This indicates that fewer users tend to take part in a conversation as it evolves, but those who do are more active (Fig. 1b ). Regarding patterns and values, the trends in user participation for various topics are consistent across each platform. According to the Mann–Kendall test, the only exceptions were Usenet Conspiracy and Talk, for which an ambiguous trend was detected. However, we note that their regression slopes are negative, suggesting a decreasing trend, even if with a weaker effect. Overall, our first set of findings highlights the shared nature of certain online interactions, revealing a decrease in user participation over time but an increase in activity among participants. This insight, consistent across most platforms, underscores the dynamic interplay between conversation length, user engagement and topic-driven participation.

Conversation size and toxicity

To detect the presence of toxic language, we used Google’s Perspective API 34 , a state-of-the-art toxicity classifier that has been used extensively in recent literature 29 , 38 . Perspective API defines a toxic comment as “A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion”. On the basis of this definition, the classifier assigns a toxicity score in the [0,1] range to a piece of text that can be interpreted as an estimate of the likelihood that a reader would perceive the comment as toxic ( https://developers.perspectiveapi.com/s/about-the-api-score ). To define an appropriate classification threshold, we draw from the existing literature 39 , which uses 0.6 as the threshold for considering a comment as toxic. A robustness check of our results using different threshold and classification tools is reported in the ‘Toxicity detection and validation of employed models’ section of the Methods , together with a discussion regarding potential shortcomings deriving from automatic classifiers. To further investigate the interplay between toxicity and conversation features across various platforms, our study first examines the prevalence of toxic speech in each dataset. We then analyse the occurrence of highly toxic users and conversations. Lastly, we investigate how the length of conversations correlates with the probability of encountering toxic comments. First of all, we define the toxicity of a user as the fraction of toxic comments that she/he left. Similarly, the toxicity of a thread is the fraction of toxic comments it contains. We begin by observing that, although some toxic datasets exist on unmoderated platforms such as Gab, Usenet and Voat, the prevalence of toxic speech is generally low. Indeed, the percentage of toxic comments in each dataset is mostly below 10% (Table 1 ). Moreover, the complementary cumulative distribution functions illustrated in Extended Data Fig. 2 show that the fraction of extremely toxic users is very low for each dataset (in the range between 10 −3 and 10 −4 ), and the majority of active users wrote at least one toxic comment, as reported in Supplementary Table 5 , therefore suggesting that the overall volume of toxicity is not a phenomenon limited to the activity of very few users and localized in few conversations. Indeed, the number of users versus their toxicity decreases sharply following an exponential trend. The toxicity of threads follows a similar pattern. To understand the association between the size and toxicity of a conversation, we start by grouping conversations according to their length to analyse their structural differences 40 . The grouping is implemented by means of logarithmic binning (see the ‘Logarithmic binning and conversation size’ section of the Methods ) and the evolution of the average fraction of toxic comments in threads versus the thread size intervals is reported in Fig. 2 . Notably, the resulting trends are almost all increasing, showing that, independently of the platform and topic, the longer the conversation, the more toxic it tends to be.

The mean fraction of toxic comments in conversations versus conversation size for each dataset. Trends represent the mean toxicity over each size interval and their 95% confidence interval. Size ranges are normalized to enable visual comparison of the different trends.

We assessed the increase in the trends by both performing linear regression and applying the Mann–Kendall test to ensure the statistical significance of our results (Extended Data Table 2 ). To further validate these outcomes, we shuffled the toxicity labels of comments, finding that trends are almost always non-increasing when data are randomized. Furthermore, the z -scores of the regression slopes indicate that the observed trends deviate from the mean of the distributions resulting from randomizations, being at least 2 s.d. greater in almost all cases. This provides additional evidence of a remarkable difference from randomness. The only decreasing trend is Usenet Politics. Moreover, we verified that our results are not influenced by the specific number of bins as, after estimating the same trends again with different intervals, we found that the qualitative nature of the results remains unchanged. These findings are summarized in Extended Data Table 2 . These analyses have been validated on the same data using a different threshold for identifying toxic comments and on a new dataset labelled with three different classifiers, obtaining similar results (Extended Data Fig. 5 , Extended Data Table 5 , Supplementary Fig. 1 and Supplementary Table 8 ). Finally, using a similar approach, we studied the toxicity content of conversations versus their lifetime—that is, the time elapsed between the first and last comment. In this case, most trends are flat, and there is no indication that toxicity is generally associated either with the duration of a conversation or the lifetime of user interactions (Extended Data Fig. 4 ).

Conversation evolution and toxicity

In the previous sections, we analysed the toxicity level of online conversations after their conclusion. We next focus on how toxicity evolves during a conversation and its effect on the dynamics of the discussion. The common beliefs that (1) online interactions inevitably devolve into toxic exchanges over time and (2) once a conversation reaches a certain toxicity threshold, it would naturally conclude, are not modern notions but they were also prevalent in the early days of the World Wide Web 41 . Assumption 2 aligns with the Perspective API’s definition of toxic language, suggesting that increased toxicity reduces the likelihood of continued participation in a conversation. However, this observation should be reconsidered, as it is not only the peak levels of toxicity that might influence a conversation but, for example, also a consistent rate of toxic content. To test these common assumptions, we used a method similar to that used for measuring participation; we select sufficiently long threads, divide each of them into a fixed number of equal intervals, compute the fraction of toxic comments for each of these intervals, average it over all threads and plot the toxicity trend through the unfolding of the conversations. We find that the average toxicity level remains mostly stable throughout, without showing a distinctive increase around the final part of threads (Fig. 3a (bottom) and Extended Data Fig. 3 ). Note that a similar observation was made previously 41 , but referring only to Reddit. Our findings challenge the assumption that toxicity discourages people from participating in a conversation, even though this notion is part of the definition of toxicity used by the detection tool. This can be seen by checking the relationship between trends in user participation, a quantity related to the number of users in a discussion at some point, and toxicity. The fact that the former typically decreases while the latter remains stable during conversations indicates that toxicity is not associated with participation in conversations (an example is shown in Fig. 3a ; box plots of the slopes of participation and toxicity for the whole dataset are shown in Fig. 3b ). This suggests that, on average, people may leave discussions regardless of the toxicity of the exchanges. We calculated the Pearson’s correlation between user participation and toxicity trends for each dataset to support this hypothesis. As shown in Fig. 3d , the resulting correlation coefficients are very heterogeneous, indicating no consistent pattern across different datasets. To further validate this analysis, we tested the differences in the participation of users commenting on either toxic or non-toxic conversations. To split such conversations into two disjoint sets, we first compute the toxicity distribution T i of long threads in each dataset i , and we then label a conversation j in dataset i as toxic if it has toxicity t ij  ≥  µ ( T i ) +  σ ( T i ), with µ ( T i ) being mean and σ ( T i ) the standard deviation of T i ; all of the other conversations are considered to be non-toxic. After splitting the threads, for each dataset, we compute the Pearson’s correlation of user participation between sets to find strongly positive values of the coefficient in all cases (Fig. 3c,e ). This result is also confirmed by a different analysis of which the results are reported in Supplementary Table 8 , in which no significant difference between slopes in toxic and non-toxic threads can be found. Thus, user behaviour in toxic and non-toxic conversations shows almost identical patterns in terms of participation. This reinforces our finding that toxicity, on average, does not appear to affect the likelihood of people participating in a conversation. These analyses were repeated with a lower toxicity classification threshold (Extended Data Fig. 5 ) and on additional datasets (Supplementary Fig. 2 and Supplementary Table 11 ), finding consistent results.

a , Examples of a typical trend in averaged user participation (top) and toxicity (bottom) versus the normalized position of comment intervals in the threads (Twitter news dataset). b , Box plot distributions of toxicity ( n  = 25, minimum = −0.012, maximum = 0.015, lower whisker = −0.012, quartile 1 (Q1) = − 0.004, Q2 = 0.002, Q3 = 0.008, upper whisker = 0.015) and participation ( n  = 25, minimum = −0.198, maximum = −0.022, lower whisker = −0.198, Q1 = − 0.109, Q2 = − 0.071, Q3 = − 0.049, upper whisker = −0.022) trend slopes for all datasets, as resulting from linear regression. c , An example of user participation in toxic and non-toxic thread sets (Twitter news dataset). d , Pearson’s correlation coefficients between user participation and toxicity trends for each dataset. e , Pearson’s correlation coefficients between user participation in toxic and non-toxic threads for each dataset.

Controversy and toxicity

In this section, we aim to explore why people participate in toxic online conversations and why longer discussions tend to be more toxic. Several factors could be the subject matter. First, controversial topics might lead to longer, more heated debates with increased toxicity. Second, the endorsement of toxic content by other users may act as an incentive to increase the discussion’s toxicity. Third, engagement peaks, due to factors such as reduced discussion focus or the intervention of trolls, may bring a higher share of toxic exchanges. Pursuing this line of inquiry, we identified proxies to measure the level of controversy in conversations and examined how these relate to toxicity and conversation size. Concurrently, we investigated the relationship between toxicity, endorsement and engagement.

As shown previously 24 , 42 , controversy is likely to emerge when people with opposing views engage in the same debate. Thus, the presence of users with diverse political leanings within a conversation could be a valid proxy for measuring controversy. We operationalize this definition as follows. Exploiting the peculiarities of our data, we can infer the political leaning of a subset of users in the Facebook News, Twitter News, Twitter Vaccines and Gab Feed datasets. This is achieved by examining the endorsement, for example, in the form of likes, expressed towards news outlets of which the political inclinations have been independently assessed by news rating agencies (see the ‘Polarization and user leaning attribution’ section of the Methods ). Extended Data Table 3 shows a breakdown of the datasets. As a result, we label users with a leaning score l   ∈  [−1, 1], −1 being left leaning and +1 being right leaning. We then select threads with at least ten different labelled users, in which at least 10% of comments (with a minimum of 20) are produced by such users and assign to each of these comments the same leaning score of those who posted them. In this setting, the level of controversy within a conversation is assumed to be captured by the spread of the political leaning of the participants in the conversation. A natural way for measuring such a spread is the s.d. σ ( l ) of the distribution of comments possessing a leaning score: the higher the σ ( l ), the greater the level of ideological disagreement and therefore controversy in a thread. We analysed the relationship between controversy and toxicity in online conversations of different sizes. Figure 4a shows that controversy increases with the size of conversations in all datasets, and its trends are positively correlated with the corresponding trends in toxicity (Extended Data Table 3 ). This supports our hypothesis that controversy and toxicity are closely related in online discussions.

a , The mean controversy ( σ ( l )) and mean toxicity versus thread size (log-binned and normalized) for the Facebook news, Twitter news, Twitter vaccines and Gab feed datasets. Here toxicity is calculated in the same conversations in which controversy could be computed (Extended Data Table 3 ); the relative Pearson’s, Spearman’s and Kendall’s correlation coefficients are also provided in Extended Data Table 3 . Trends are reported with their 95% confidence interval. b , Likes/upvotes versus toxicity (linearly binned). c , An example (Voat politics dataset) of the distributions of the frequency of toxic comments in threads before ( n  = 2,201, minimum = 0, maximum = 1, lower whisker = 0, Q1 = 0, Q2 = 0.15, Q3 = 0.313, upper whisker = 0.769) at the peak ( n  = 2,798, minimum = 0, maximum = 0.8, lower whisker = 0, Q1 = 0.125, Q2 = 0.196, Q3 = 0.282, upper whisker = 0.513) and after the peak ( n  = 2,791, minimum = 0, maximum = 1, lower whisker = 0, Q1 = 0.129, Q2 = 0.200, Q3 = 0.282, upper whisker = 0.500) of activity, as detected by Kleinberg’s burst detection algorithm.

As a complementary analysis, we draw on previous results 43 . In that study, using a definition of controversy operationally different but conceptually related to ours, a link was found between a greater degree of controversy of a discussion topic and a wider distribution of sentiment scores attributed to the set of its posts and comments. We quantified the sentiment of comments using a pretrained BERT model available from Hugging Face 44 , used also in previous studies 45 . The model predicts the sentiment of a sentence through a scoring system ranging from 1 (negative) to 5 (positive). We define the sentiment attributed to a comment c as its weighted mean \(s(c)=\sum _{i=1.5}{x}_{i}{p}_{i}\) , where x i   ∈  [1, 5] is the output score from the model and p i is the probability associated to that value. Moreover, we normalize the sentiment score s for each dataset between 0 and 1. We observe the trends of the mean s.d. of sentiment in conversations, \(\bar{\sigma }(s)\) , and toxicity are positively correlated for moderated platforms such as Facebook and Twitter but are negatively correlated on Gab (Extended Data Table 3 ). The positive correlation observed in Facebook and Twitter indicates that greater discrepancies in sentiment of the conversations can, in general, be linked to toxic conversations and vice versa. Instead, on unregulated platforms such as Gab, highly conflicting sentiments seem to be more likely to emerge in less toxic conversations.

As anticipated, another factor that may be associated with the emergence of toxic comments is the endorsement they receive. Indeed, such positive reactions may motivate posting even more comments of the same kind. Using the mean number of likes/upvotes as a proxy of endorsement, we have an indication that this may not be the case. Figure 4b shows that the trend in likes/upvotes versus comments toxicity is never increasing past the toxicity score threshold (0.6).

Finally, to complement our analysis, we inspect the relationship between toxicity and user engagement within conversations, measured as the intensity of the number of comments over time. To do so, we used a method for burst detection 46 that, after reconstructing the density profile of a temporal stream of elements, separates the stream into different levels of intensity and assigns each element to the level to which it belongs (see the ‘Burst analysis’ section of the Methods ). We computed the fraction of toxic comments at the highest intensity level of each conversation and for the levels right before and after it. By comparing the distributions of the fraction of toxic comments for the three intervals, we find that these distributions are statistically different in almost all cases (Fig. 4c and Extended Data Table 4 ). In all datasets but one, distributions are consistently shifted towards higher toxicity at the peak of engagement, compared with the previous phase. Likewise, in most cases, the peak shows higher toxicity even if compared to the following phase, which in turn is mainly more toxic than the phase before the peak. These results suggest that toxicity is likely to increase together with user engagement.

Here we examine one of the most prominent and persistent characteristics online discussions—toxic behaviour, defined here as rude, disrespectful or unreasonable conduct. Our analysis suggests that toxicity is neither a deterrent to user involvement nor an engagement amplifier; rather, it tends to emerge when exchanges become more frequent and may be a product of opinion polarization. Our findings suggest that the polarization of user opinions—intended as the degree of opposed partisanship of users in a conversation—may have a more crucial role than toxicity in shaping the evolution of online discussions. Thus, monitoring polarization could indicate early interventions in online discussions. However, it is important to acknowledge that the dynamics at play in shaping online discourse are probably multifaceted and require a nuanced approach for effective moderation. Other factors may influence toxicity and engagement, such as the specific subject of the conversation, the presence of influential users or ‘trolls’, the time and day of posting, as well as cultural or demographic aspects, such as user average age or geographical location. Furthermore, even though extremely toxic users are rare (Extended Data Fig. 2 ), the relationship between participation and toxicity of a discussion may in principle be affected also by small groups of highly toxic and engaged users driving the conversation dynamics. Although the analysis of such subtler aspects is beyond the scope of this Article, they are certainly worth investigating in future research.

However, when people encounter views that contradict their own, they may react with hostility and contempt, consistent with previous research 47 . In turn, it may create a cycle of negative emotions and behaviours that fuels toxicity. We also show that some online conversation features have remained consistent over the past three decades despite the evolution of platforms and social norms.

Our study has some limitations that we acknowledge and discuss. First, we use political leaning as a proxy for general leaning, which may capture only some of the nuances of online opinions. However, political leaning represents a broad spectrum of opinions across different topics, and it correlates well with other dimensions of leaning, such as news preferences, vaccine attitudes and stance on climate change 48 , 49 . We could not assign a political leaning to users to analyse controversies on all platforms. Still, those considered—Facebook, Gab and Twitter—represent different populations and moderation policies, and the combined data account for nearly 90% of the content in our entire dataset. Our analysis approach is based on breadth and heterogeneity. As such, it may raise concerns about potential reductionism due to the comparison of different datasets from different sources and time periods. We acknowledge that each discussion thread, platform and context has unique characteristics and complexities that might be diminished when homogenizing data. However, we aim not to capture the full depth of every discussion but to identify and highlight general patterns and trends in online toxicity across platforms and time. The quantitative approach used in our study is similar to numerous other studies 15 and enables us to uncover these overarching principles and patterns that may otherwise remain hidden. Of course, it is not possible to account for the behaviours of passive users. This entails, for example, that even if toxicity does not seem to make people leave conversations, it could still be a factor that discourages them from joining them. Our study leverages an extensive dataset to examine the intricate relationship between persistent online human behaviours and the characteristics of different social media platforms. Our findings challenge the prevailing assumption by demonstrating that toxic content, as traditionally defined, does not necessarily reduce user engagement, thereby questioning the assumed direct correlation between toxic content and negative discourse dynamics. This highlights the necessity for a detailed examination of the effect of toxic interactions on user behaviour and the quality of discussions across various platforms. Our results, showing user resilience to toxic content, indicate the potential for creating advanced, context-aware moderation tools that can accurately navigate the complex influence of antagonistic interactions on community engagement and discussion quality. Moreover, our study sets the stage for further exploration into the complexities of toxicity and its effect on engagement within online communities. Advancing our grasp of online discourse necessitates refining content moderation techniques grounded in a thorough understanding of human behaviour. Thus, our research adds to the dialogue on creating more constructive online spaces, promoting moderation approaches that are effective yet nuanced, facilitating engaging exchanges and reducing the tangible negative effects of toxic behaviour.

Through the extensive dataset presented here, critical aspects of the online platform ecosystem and fundamental dynamics of user interactions can be explored. Moreover, we provide insights that a comparative approach such as the one followed here can prove invaluable in discerning human behaviour from platform-specific features. This may be used to investigate further sensitive issues, such as the formation of polarization and misinformation. The resulting outcomes have multiple potential impacts. Our findings reveal consistent toxicity patterns across platforms, topics and time, suggesting that future research in this field should prioritize the concept of invariance. Recognizing that toxic behaviour is a widespread phenomenon that is not limited by platform-specific features underscores the need for a broader, unified approach to understanding online discourse. Furthermore, the participation of users in toxic conversations suggests that a simple approach to removing toxic comments may not be sufficient to prevent user exposure to such phenomena. This indicates a need for more sophisticated moderation techniques to manage conversation dynamics, including early interventions in discussions that show warnings of becoming toxic. Furthermore, our findings support the idea that examining content pieces in connection with others could enhance the effectiveness of automatic toxicity detection models. The observed homogeneity suggests that models trained using data from one platform may also have applicability to other platforms. Future research could explore further into the role of controversy and its interaction with other elements contributing to toxicity. Moreover, comparing platforms could enhance our understanding of invariant human factors related to polarization, disinformation and content consumption. Such studies would be instrumental in capturing the drivers of the effect of social media platforms on human behaviour, offering valuable insights into the underlying dynamics of online interactions.

Data collection

In our study, data collection from various social media platforms was strategically designed to encompass various topics, ensuring maximal heterogeneity in the discussion themes. For each platform, where feasible, we focus on gathering posts related to diverse areas such as politics, news, environment and vaccinations. This approach aims to capture a broad spectrum of discourse, providing a comprehensive view of conversation dynamics across different content categories.

We use datasets from previous studies that covered discussions about vaccines 50 , news 51 and brexit 52 . For the vaccines topic, the resulting dataset contains around 2 million comments retrieved from public groups and pages in a period that ranges from 2 January 2010 to 17 July 2017. For the news topic, we selected a list of pages from the Europe Media Monitor that reported the news in English. As a result, the obtained dataset contains around 362 million comments between 9 September 2009 and 18 August 2016. Furthermore, we collect a total of about 4.5 billion likes that the users put on posts and comments concerning these pages. Finally, for the brexit topic, the dataset contains around 460,000 comments from 31 December 2015 to 29 July 2016.

We collect data from the Pushshift.io archive ( https://files.pushshift.io/gab/ ) concerning discussions taking place from 10 August 2016, when the platform was launched, to 29 October 2018, when Gab went temporarily offline due to the Pittsburgh shooting 53 . As a result, we collect a total of around 14 million comments.

Data were collected from the Pushshift.io archive ( https://pushshift.io/ ) for the period ranging from 1 January 2018 to 31 December 2022. For each topic, whenever possible, we manually identified and selected subreddits that best represented the targeted topics. As a result of this operation, we obtained about 800,000 comments from the r/conspiracy subreddit for the conspiracy topic. For the vaccines topic, we collected about 70,000 comments from the r/VaccineDebate subreddit, focusing on the COVID-19 vaccine debate. We collected around 400,000 comments from the r/News subreddit for the news topic. We collected about 70,000 comments from the r/environment subreddit for the climate change topic. Finally, we collected around 550,000 comments from the r/science subreddit for the science topic.

We created a list of 14 channels, associating each with one of the topics considered in the study. For each channel, we manually collected messages and their related comments. As a result, from the four channels associated with the news topic (news notiziae, news ultimora, news edizionestraordinaria, news covidultimora), we obtained around 724,000 comments from posts between 9 April 2018 and 20 December 2022. For the politics topic, instead, the corresponding two channels (politics besttimeline, politics polmemes) produced a total of around 490,000 comments between 4 August 2017 and 19 December 2022. Finally, the eight channels assigned to the conspiracy topic (conspiracy bennyjhonson, conspiracy tommyrobinsonnews, conspiracy britainsfirst, conspiracy loomeredofficial, conspiracy thetrumpistgroup, conspiracy trumpjr, conspiracy pauljwatson, conspiracy iononmivaccino) produced a total of about 1.4 million comments between 30 August 2019 and 20 December 2022.

We used a list of datasets from previous studies that includes discussions about vaccines 54 , climate change 49 and news 55 topics. For the vaccines topic, we collected around 50 million comments from 23 January 2010 to 25 January 2023. For the news topic, we extend the dataset used previously 55 by collecting all threads composed of less than 20 comments, obtaining a total of about 9.5 million comments for a period ranging from 1 January 2020 to 29 November 2022. Finally, for the climate change topic, we collected around 9.7 million comments between 1 January 2020 and 10 January 2023.

We collected data for the Usenet discussion system by querying the Usenet Archive ( https://archive.org/details/usenet?tab=about ). We selected a list of topics considered adequate to contain a large, broad and heterogeneous number of discussions involving active and populated newsgroups. As a result of this selection, we selected conspiracy, politics, news and talk as topic candidates for our analysis. For the conspiracy topic, we collected around 280,000 comments between 1 September 1994 and 30 December 2005 from the alt.conspiracy newsgroup. For the politics topics, we collected around 2.6 million comments between 29 June 1992 and 31 December 2005 from the alt.politics newsgroup. For the news topic, we collected about 620,000 comments between 5 December 1992 and 31 December 2005 from the alt.news newsgroup. Finally, for the talk topic, we collected all of the conversations from the homonym newsgroup on a period that ranges from 13 February 1989 to 31 December 2005 for around 2.1 million contents.

We used a dataset presented previously 56 that covers the entire lifetime of the platform, from 9 January 2018 to 25 December 2020, including a total of around 16.2 million posts and comments shared by around 113,000 users in about 7,100 subverses (the equivalent of a subreddit for Voat). Similarly to previous platforms, we associated the topics to specific subverses. As a result of this operation, for the conspiracy topic, we collected about 1 million comments from the greatawakening subverse between 9 January 2018 and 25 December 2020. For the politics topic, we collected around 1 million comments from the politics subverse between 16 June 2014 and 25 December 2020. Finally, for the news topic, we collected about 1.4 million comments from the news subverse between 21 November 2013 and 25 December 2020.

We used a dataset proposed in previous studies that collected conversations about the climate change topic 49 , which is extended, coherently with previous platforms, by including conversations about vaccines and news topics. The data collection process for YouTube is performed using the YouTube Data API ( https://developers.google.com/youtube/v3 ). For the climate change topic, we collected around 840,000 comments between 16 March 2014 and 28 February 2022. For the vaccines topic, we collected conversations between 31 January 2020 and 24 October 2021 containing keywords about COVID-19 vaccines, namely Sinopharm, CanSino, Janssen, Johnson&Johnson, Novavax, CureVac, Pfizer, BioNTech, AstraZeneca and Moderna. As a result of this operation, we gathered a total of around 2.6 million comments to videos. Finally, for the news topic, we collected about 20 million comments between 13 February 2006 and 8 February 2022, including videos and comments from a list of news outlets, limited to the UK and provided by Newsguard (see the ‘Polarization and user leaning attribution’ section).

Content moderation policies

Content moderation policies are guidelines that online platforms use to monitor the content that users post on their sites. Platforms have different goals and audiences, and their moderation policies may vary greatly, with some placing more emphasis on free expression and others prioritizing safety and community guidelines.

Facebook and YouTube have strict moderation policies prohibiting hate speech, violence and harassment 57 . To address harmful content, Facebook follows a ‘remove, reduce, inform’ strategy and uses a combination of human reviewers and artificial intelligence to enforce its policies 58 . Similarly, YouTube has a similar set of community guidelines regarding hate speech policy, covering a wide range of behaviours such as vulgar language 59 , harassment 60 and, in general, does not allow the presence of hate speech and violence against individuals or groups based on various attributes 61 . To ensure that these guidelines are respected, the platform uses a mix of artificial intelligence algorithms and human reviewers 62 .

Twitter also has a comprehensive content moderation policy and specific rules against hateful conduct 63 , 64 . They use automation 65 and human review in the moderation process 66 . At the date of submission, Twitter’s content policies have remained unchanged since Elon Musk’s takeover, except that they ceased enforcing their COVID-19 misleading information policy on 23 November 2022. Their policy enforcement has faced criticism for inconsistency 67 .

Reddit falls somewhere in between regarding how strict its moderation policy is. Reddit’s content policy has eight rules, including prohibiting violence, harassment and promoting hate based on identity or vulnerability 68 , 69 . Reddit relies heavily on user reports and volunteer moderators. Thus, it could be considered more lenient than Facebook, YouTube and Twitter regarding enforcing rules. In October 2022, Reddit announced that they intend to update their enforcement practices to apply automation in content moderation 70 .

By contrast, Telegram, Gab and Voat take a more hands-off approach with fewer restrictions on content. Telegram has ambiguity in its guidelines, which arises from broad or subjective terms and can lead to different interpretations 71 . Although they mentioned they may use automated algorithms to analyse messages, Telegram relies mainly on users to report a range of content, such as violence, child abuse, spam, illegal drugs, personal details and pornography 72 . According to Telegram’s privacy policy, reported content may be checked by moderators and, if it is confirmed to violate their terms, temporary or permanent restrictions may be imposed on the account 73 . Gab’s Terms of Service allow all speech protected under the First Amendment to the US Constitution, and unlawful content is removed. They state that they do not review material before it is posted on their website and cannot guarantee prompt removal of illegal content after it has been posted 74 . Voat was once known as a ‘free-speech’ alternative to Reddit and allowed content even if it may be considered offensive or controversial 56 .

Usenet is a decentralized online discussion system created in 1979. Owing to its decentralized nature, Usenet has been difficult to moderate effectively, and it has a reputation for being a place where controversial and even illegal content can be posted without consequence. Each individual group on Usenet can have its own moderators, who are responsible for monitoring and enforcing their group’s rules, and there is no single set of rules that applies to the entire platform 75 .

Logarithmic binning and conversation size

Owing to the heavy-tailed distributions of conversation length (Extended Data Fig. 1 ), to plot the figures and perform the analyses, we used logarithmic binning. Thus, according to its length, each thread of each dataset is assigned to 1 out of 21 bins. To ensure a minimal number of points in each bin, we iteratively change the left bound of the last bin so that it contains at least N  = 50 elements (we set N  = 100 in the case of Facebook news, due to its larger size). Specifically, considering threads ordered in increasing length, the size of the largest thread is changed to that of the second last largest one, and the binning is recalculated accordingly until the last bin contains at least N points.

For visualization purposes, we provide a normalization of the logarithmic binning outcome that consists of mapping discrete points into coordinates of the x axis such that the bins correspond to {0, 0.05, 0.1, ..., 0.95, 1}.

To perform the part of the analysis, we select conversations belonging to the [0.7, 1] interval of the normalized logarithmic binning of thread length. This interval ensures that the conversations are sufficiently long and that we have a substantial number of threads. Participation and toxicity trends are obtained by applying to such conversations a linear binning of 21 elements to a chronologically ordered sequence of comments, that is, threads. A breakdown of the resulting datasets is provided in Supplementary Table 2 .

Finally, to assess the equality of the growth rates of participation values in toxic and non-toxic threads (see the ‘Conversation evolution and toxicity’ section), we implemented the following linear regression model:

where the term β 2 accounts for the effect that being a toxic conversation has on the growth of participation. Our results show that β 2 is not significantly different from 0 in most original and validation datasets (Supplementary Tables 8 and 11 )

Toxicity detection and validation of the models used

The problem of detecting toxicity is highly debated, to the point that there is currently no agreement on the very definition of toxic speech 64 , 76 . A toxic comment can be regarded as one that includes obscene or derogatory language 32 , that uses harsh, abusive language and personal attacks 33 , or contains extremism, violence and harassment 11 , just to give a few examples. Even though toxic speech should, in principle, be distinguished from hate speech, which is commonly more related to targeted attacks that denigrate a person or a group on the basis of attributes such as race, religion, gender, sex, sexual orientation and so on 77 , it sometimes may also be used as an umbrella term 78 , 79 . This lack of agreement directly reflects the challenging and inherent subjective nature of the concept of toxicity. The complexity of the topic makes it particularly difficult to assess the reliability of natural language processing models for automatic toxicity detection despite the impressive improvements in the field. Modern natural language processing models, such as Perspective API, are deep learning models that leverage word-embedding techniques to build representations of words as vectors in a high-dimensional space, in which a metric distance should reflect the conceptual distance among words, therefore providing linguistic context. A primary concern regarding toxicity detection models is their limited ability to contextualize conversations 11 , 80 . These models often struggle to incorporate factors beyond the text itself, such as the participant’s personal characteristics, motivations, relationships, group memberships and the overall tone of the discussion 11 . Consequently, what is considered to be toxic content can vary significantly among different groups, such as ethnicities or age groups 81 , leading to potential biases. These biases may stem from the annotators’ backgrounds and the datasets used for training, which might not adequately represent cultural heterogeneity. Moreover, subtle forms of toxic content, like indirect allusions, memes and inside jokes targeted at specific groups, can be particularly challenging to detect. Word embeddings equip current classifiers with a rich linguistic context, enhancing their ability to recognize a wide range of patterns characteristic of toxic expression. However, the requirements for understanding the broader context of a conversation, such as personal characteristics, motivations and group dynamics, remain beyond the scope of automatic detection models. We acknowledge these inherent limitations in our approach. Nonetheless, reliance on automatic detection models is essential for large-scale analyses of online toxicity like the one conducted in this study. We specifically resort to the Perspective API for this task, as it represents state-of-the-art automatic toxicity detection, offering a balance between linguistic nuance and scalable analysis capabilities. To define an appropriate classification threshold, we draw from the existing literature 64 , which uses 0.6 as the threshold for considering a comment to be toxic. This threshold can also be considered a reasonable one as, according to the developer guidelines offered by Perspective, it would indicate that the majority of the sample of readers, namely 6 out of 10, would perceive that comment as toxic. Due to the limitations mentioned above (for a criticism of Perspective API, see ref. 82 ), we validate our results by performing a comparative analysis using two other toxicity detectors: Detoxify ( https://github.com/unitaryai/detoxify ), which is similar to Perspective, and IMSYPP, a classifier developed for a European Project on hate speech 16 ( https://huggingface.co/IMSyPP ). In Supplementary Table 14 , the percentages of agreement among the three models in classifying 100,000 comments taken randomly from each of our datasets are reported. For Detoxify we used the same binary toxicity threshold (0.6) as used with Perspective. Although IMSYPP operates on a distinct definition of toxicity as outlined previously 16 , our comparative analysis shows a general agreement in the results. This alignment, despite the differences in underlying definitions and methodologies, underscores the robustness of our findings across various toxicity detection frameworks. Moreover, we perform the core analyses of this study using all classifiers on a further, vast and heterogeneous dataset. As shown in Supplementary Figs. 1 and 2 , the results regarding toxicity increase with conversation size and user participation and toxicity are quantitatively very similar. Furthermore, we verify the stability of our findings under different toxicity thresholds. Although the main analyses in this paper use the threshold value recommended by the Perspective API, set at 0.6, to minimize false positives, our results remain consistent even when applying a less conservative threshold of 0.5. This is demonstrated in Extended Data Fig. 5 , confirming the robustness of our observations across varying toxicity levels. For this study, we used the API support for languages prevalent in the European and American continents, including English, Spanish, French, Portuguese, German, Italian, Dutch, Polish, Swedish and Russian. Detoxify also offers multilingual support. However, IMSYPP is limited to English and Italian text, a factor considered in our comparative analysis.

Polarization and user leaning attribution

Our approach to measuring controversy in a conversation is based on estimating the degree of political partisanship among the participants. This measure is closely related to the political science concept of political polarization. Political polarization is the process by which political attitudes diverge from moderate positions and gravitate towards ideological extremes, as described previously 83 . By quantifying the level of partisanship within discussions, we aim to provide insights into the extent and nature of polarization in online debates. In this context, it is important to distinguish between ‘ideological polarization’ and ‘affective polarization’. Ideological polarization refers to divisions based on political viewpoints. By contrast, affective polarization is characterized by positive emotions towards members of one’s group and hostility towards those of opposing groups 84 , 85 . Here we focus specifically on ideological polarization. The subsequent description of our procedure for attributing user political leanings will further clarify this focus. On online social media, the individual leaning of a user toward a topic can be inferred through the content produced or the endorsement shown toward specific content. In this study, we consider the endorsement of users to news outlets of which the political leaning has been evaluated by trustworthy external sources. Although not without limitations—which we address below—this is a standard approach that has been used in several studies, and has become a common and established practice in the field of social media analysis due to its practicality and effectiveness in providing a broad understanding of political dynamics on these online platforms 1 , 43 , 86 , 87 , 88 . We label news outlets with a political score based on the information reported by Media Bias/Fact Check (MBFC) ( https://mediabiasfactcheck.com ), integrating with the equivalent information from Newsguard ( https://www.newsguardtech.com/ ). MBFC is an independent fact-checking organization that rates news outlets on the basis of the reliability and the political bias of the content that they produce and share. Similarly, Newsguard is a tool created by an international team of journalists that provides news outlet trust and political bias scores. Following standard methods used in the literature 1 , 43 , we calculated the individual leaning of a user l   ∈  [−1, 1] as the average of the leaning scores l c   ∈  [−1, 1] attributed to each of the content it produced/shared, where l c results from a mapping of the news organizations political scores provided by MBFC and Newsguard, respectively: [left, centre-left, centre, centre-right, right] to [−1, − 0.5, 0, 0.5, 1], and [far left, left, right, far right] to [−1, −0.5, 0.5, 1]). Our datasets have different structures, so we have to evaluate user leanings in different ways. For Facebook News, we assign a leaning score to users who posted a like at least three times and commented at least three times under news outlet pages that have a political score. For Twitter News, a leaning is assigned to users who posted at least 15 comments under scored news outlet pages. For Twitter Vaccines and Gab, we consider users who shared content produced by scored news outlet pages at least three times. A limitation of our approach is that engaging with politically aligned content does not always imply agreement; users may interact with opposing viewpoints for critical discussion. However, research indicates that users predominantly share content aligning with their own views, especially in politically charged contexts 87 , 89 , 90 . Moreover, our method captures users who actively express their political leanings, omitting the ‘passive’ ones. This is due to the lack of available data on users who do not explicitly state their opinions. Nevertheless, analysing active users offers valuable insights into the discourse of those most engaged and influential on social media platforms.

Burst analysis

We used the Kleinberg burst detection algorithm 46 (see the ‘Controversy and toxicity’ section) to all conversations with at least 50 comments in a dataset. In our analysis, we randomly sample up to 5,000 conversations, each containing a specific number of comments. To ensure the reliability of our data, we exclude conversations with an excessive number of double timestamps—defined as more than 10 consecutive or over 100 within the first 24 h. This criterion helps to mitigate the influence of bots, which could distort the patterns of human activity. Furthermore, we focus on the first 24 h of each thread to analyse streams of comments during their peak activity period. Consequently, Usenet was excluded from our study. The unique usage characteristics of Usenet render such a time-constrained analysis inappropriate, as its activity patterns do not align with those of the other platforms under consideration. By reconstructing the density profile of the comment stream, the algorithm divides the entire stream’s interval into subintervals on the basis of their level of intensity. Labelled as discrete positive values, higher levels of burstiness represent higher activity segments. To avoid considering flat-density phases, threads with a maximum burst level equal to 2 are excluded from this analysis. To assess whether a higher intensity of comments results in a higher comment toxicity, we perform a Mann–Whitney U -test 91 with Bonferroni correction for multiple testing between the distributions of the fraction of toxic comments t i in three intensity phases: during the peak of engagement and at the highest levels before and after. Extended Data Table 4 shows the corrected P values of each test, at a 0.99 confidence level, with H1 indicated in the column header. An example of the distribution of the frequency of toxic comments in threads at the three phases of a conversation considered (pre-peak, peak and post-peak) is reported in Fig. 4c .

Toxicity detection on Usenet

As discussed in the section on toxicity detection and the Perspective API above, automatic detectors derive their understanding of toxicity from the annotated datasets that they are trained on. The Perspective API is predominantly trained on recent texts, and its human labellers conform to contemporary cultural norms. Thus, although our dataset dates back to no more than the early 1990s, we provide a discussion on the viability of the application of Perspective API to Usenet and validation analysis. Contemporary society, especially in Western contexts, is more sensitive to issues of toxicity, including gender, race and sexual orientation, compared with a few decades ago. This means that some comments identified as toxic today, including those from older platforms like Usenet, might not have been considered as such in the past. However, this discrepancy does not significantly affect our analysis, which is centred on current standards of toxicity. On the other hand, changes in linguistic features may have some repercussions: there may be words and locutions that were frequently used in the 1990s that instead appear sparsely in today’s language, making Perspective potentially less effective in classifying short texts that contain them. We therefore proceeded to evaluate the impact that such a possible scenario could have on our results. In light of the above considerations, we consider texts labelled as toxic as correctly classified; instead, we assume that there is a fixed probability p that a comment may be incorrectly labelled as non-toxic. Consequently, we randomly designate a proportion p of non-toxic comments, relabel them as toxic and compute the toxicity versus conversation size trend (Fig. 2 ) on the altered dataset across various p . Specifically, for each value, we simulate 500 different trends, collecting their regression slopes to obtain a null distribution for them. To assess if the probability of error could lead to significant differences in the observed trend, we compute the fraction f of slopes lying outside the interval (−| s |,| s |), where s is the slope of the observed trend. We report the result in Supplementary Table 9 for different values of p . In agreement with our previous analysis, we assume that the slope differs significantly from the ones obtained from randomized data if f is less than 0.05.

We observed that only the Usenet Talk dataset shows sensitivity to small error probabilities, and the others do not show a significant difference. Consequently, our results indicate that Perspective API is suitable for application to Usenet data in our analyses, notwithstanding the potential linguistic and cultural shifts that might affect the classifier’s reliability with older texts.

Toxicity of short conversations

Our study focuses on the relationship between user participation and the toxicity of conversations, particularly in engaged or prolonged discussions. A potential concern is that concentrating on longer threads overlooks conversations that terminate quickly due to early toxicity, therefore potentially biasing our analysis. To address this, we analysed shorter conversations, comprising 6 to 20 comments, in each dataset. In particular, we computed the distributions of toxicity scores of the first and last three comments in each thread. This approach helps to ensure that our analysis accounts for a range of conversation lengths and patterns of toxicity development, providing a more comprehensive understanding of the dynamics at play. As shown in Supplementary Fig. 3 , for each dataset, the distributions of the toxicity scores display high similarity, meaning that, in short conversations, the last comments are not significantly more toxic than the initial ones, indicating that the potential effects mentioned above do not undermine our conclusions. Regarding our analysis of longer threads, we notice here that the participation quantity can give rise to similar trends in various cases. For example, high participation can be achieved because many users take part in the conversation, but also with small groups of users in which everyone is equally contributing over time. Or, in very large discussions, the contributions of individual outliers may remain hidden. By measuring participation, these and other borderline cases may not be distinct from the statistically highly likely discussion dynamics but, ultimately, this lack of discriminatory power does not have any implications on our findings nor on the validity of the conclusions that we draw.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Facebook, Twitter and YouTube data are made available in accordance with their respective terms of use. IDs of comments used in this work are provided at Open Science Framework ( https://doi.org/10.17605/osf.io/fq5dy ). For the remaining platforms (Gab, Reddit, Telegram, Usenet and Voat), all of the necessary information to recreate the datasets used in this study can be found in the ‘Data collection’ section.

Code availability

The code used for the analyses presented in the Article is available at Open Science Framework ( https://doi.org/10.17605/osf.io/fq5dy ). The repository includes dummy datasets to illustrate the required data format and make the code run.

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Acknowledgements

We thank M. Samory for discussions; T. Quandt and Z. Zhang for suggestions during the review process; and Geronimo Stilton and the Hypnotoad for inspiring the data analysis and result interpretation. The work is supported by IRIS Infodemic Coalition (UK government, grant no. SCH-00001-3391), SERICS (PE00000014) under the NRRP MUR program funded by the EU NextGenerationEU project CRESP from the Italian Ministry of Health under the program CCM 2022, PON project ‘Ricerca e Innovazione’ 2014-2020, and PRIN Project MUSMA for Italian Ministry of University and Research (MUR) through the PRIN 2022CUP G53D23002930006 and EU Next-Generation EU, M4 C2 I1.1.

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These authors contributed equally: Michele Avalle, Niccolò Di Marco, Gabriele Etta

Authors and Affiliations

Department of Computer Science, Sapienza University of Rome, Rome, Italy

Michele Avalle, Niccolò Di Marco, Gabriele Etta, Shayan Alipour, Lorenzo Alvisi, Matteo Cinelli & Walter Quattrociocchi

Department of Social Sciences and Economics, Sapienza University of Rome, Rome, Italy

Emanuele Sangiorgio

Department of Communication and Social Research, Sapienza University of Rome, Rome, Italy

Anita Bonetti

Institute of Complex Systems, CNR, Rome, Italy

Antonio Scala

Department of Mathematics, City University of London, London, UK

Andrea Baronchelli

The Alan Turing Institute, London, UK

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Contributions

Conception and design: W.Q., M.A., M.C., G.E. and N.D.M. Data collection: G.E. and N.D.M. with collaboration from M.C., M.A. and S.A. Data analysis: G.E., N.D.M., M.A., M.C., W.Q., E.S., A. Bonetti, A. Baronchelli and A.S. Code writing: G.E. and N.D.M. with collaboration from M.A., E.S., S.A. and M.C. All of the authors provided critical feedback and helped to shape the research, analysis and manuscript, and contributed to the preparation of the manuscript.

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Correspondence to Matteo Cinelli or Walter Quattrociocchi .

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Extended data figures and tables

Extended data fig. 1 general characteristics of online conversations..

a . Distributions of conversation length (number of comments in a thread). b . Distributions of the time duration (days) of user activity on a platform for each platform and each topic. c . Time duration (days) distributions of threads. Colour-coded legend on the side.

Extended Data Fig. 2 Extremely toxic authors and conversations are rare.

a . Complementary cumulative distribution functions (CCDFs) of the toxicity of authors who posted more than 10 comments. Toxicity is defined as usual as the fraction of toxic comments over the total of comments posted by a user. b . CCDFs of the toxicity of conversations containing more than 10 comments. Colour-coded legend on the side.

Extended Data Fig. 3 User toxicity as conversations evolve.

Mean fraction of toxic comments as conversations progress. The x-axis represents the normalized position of comment intervals in the threads. For each dataset, toxicity is computed in the thread size interval [0.7−1] (see main text and Tab. S 2 in SI). Trends are reported with their 95% confidence interval. Colour-coded legend on the side.

Extended Data Fig. 4 Toxicity is not associated with conversation lifetime.

Mean toxicity of a . users versus their time of permanence in the dataset and b . threads versus their time duration. Trends are reported with their 95% confidence interval and they are reported using a normalized log-binning. Colour-coded legend on the side.

Extended Data Fig. 5 Results hold for a different toxicity threshold.

Core analyses presented in the paper repeated employing a lower (0.5) toxicity binary classification threshold. a . Mean fraction of toxic comments in conversations versus conversation size, for each dataset (see Fig. 2 ). Trends are reported with their 95% confidence interval. b . Pearson’s correlation coefficients between user participation and toxicity trends for each dataset. c . Pearson’s correlation coefficients between users’ participation in toxic and non-toxic thread sets, for each dataset. d . Boxplot of the distribution of toxicity ( n  = 25, min = −0.016, max = 0.020, lower whisker = −0.005, Q 1 = − 0.005, Q 2  = 0.004, Q 3  = 0.012, upper whisker = 0.020) and participation ( n  = 25, min = −0.198, max = −0.022, lower whisker = −0.198, Q 1 = − 0.109, Q 2  = − 0.070, Q 3  = − 0.049, upper whisker = −0.022) trend slopes for all datasets, as resulting from linear regression. The results of the relative Mann-Kendall tests for trend assessment are shown in Extended Data Table 5 .

Supplementary information

Supplementary information.

Supplementary Information 1–4, including details regarding data collection for validation dataset, Supplementary Figs. 1–3, Supplementary Tables 1–17 and software and coding specifications.

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Heat Waves Are Moving Slower and Staying Longer, Study Finds

Climate change is making heat waves linger for longer stretches of time, exacerbating the effects of extreme temperatures.

By Delger Erdenesanaa

When heat waves swept across large parts of the planet last summer , in many places the oppressive temperatures loitered for days or weeks at a time. As climate change warms the planet, heat waves are increasingly moving sluggishly and lasting longer, according to a study published on Friday .

Each decade between 1979 and 2020, the rate at which heat waves travel, pushed along by air circulation, slowed by about 5 miles per day, the study found. Heat waves also now last about four days longer on average.

“This really has strong impacts on public health,” said Wei Zhang, a climate scientist at Utah State University and one of the authors of the study, which appeared in the journal Science Advances.

The longer heat waves stick around in one place, the longer people are exposed to life-threatening temperatures . As workers slow down during extreme heat, so does economic productivity . Heat waves also dry out soil and vegetation, harming crops and raising the risk of wildfires.

These changes to heat wave behavior have been more noticeable since the late 1990s, Dr. Zhang said. He attributes the changes in large part to human-caused climate change, but also in part to natural climate variability.

The study is among the first to track how heat waves move through both space and time.

Rachel White, an atmospheric scientist at the University of British Columbia who wasn’t involved in the paper, said she had been waiting to see research like this.

“We know that climate change is increasing the intensity of heat waves. We know climate change is increasing the frequency of heat waves,” Dr. White said. “But this study really helps us understand more about how that’s happening.”

Dr. Zhang and his colleagues analyzed temperatures around the world between 1979 and 2020. They defined heat waves as contiguous areas reaching a total of 1 million square kilometers (247 million acres) or more, where temperatures rose to at least the 95th percentile of the local historical maximum temperature (basically, enormous blobs of unusually hot air). The heat waves also had to last for at least three days. The researchers then measured how far these giant air masses moved over time to calculate their speed.

Over all the years they studied, heat waves slowed down by about 8 kilometers per day each decade, or nearly 5 miles per day each decade.

The average life span of heat waves has also stretched out: From 2016-20, they persisted for an average of 12 days, compared with eight days from 1979 to 1983. These longer-lived heat waves are also traveling farther, increasing the distance they travel by about 226 kilometers per decade.

The researchers also found that heat waves are becoming more frequent, to an average of 98 per year between 2016 and 2020, from 75 per year between 1979 and 1983.

There are some regional differences. Heat waves are lasting longer particularly in Eurasia and North America. And they are traveling farther particularly in South America.

To examine the role of climate change, the researchers used models to simulate temperatures in scenarios with and without the warming from human greenhouse gas emissions. They found that the scenario with these emissions was the best match for what has actually happened to heat wave behavior, indicating that climate change is a major force behind these trends.

Scientists have started to detect a larger pattern of air circulation and upper atmosphere winds like the jet streams getting weaker, at least during the summer at higher latitudes in the Northern Hemisphere. This could cause extreme weather events of all kinds to stall and overstay their welcome.

“It stands to reason that that would slow down the speed of heat waves,” said Stephen Vavrus, the state climatologist for Wisconsin. Dr. Vavrus studies atmospheric circulation but wasn’t involved in this research.

The new study did find a correlation between a weaker jet stream and slower heat waves. Dr. White, however, thinks more research is needed to determine whether the jet stream is truly the cause.

No matter the exact reasons for the slowdown, the harmful effects remain.

“It’s sort of multiple factors conspiring together,” Dr. Vavrus said. If heat waves become more frequent, more intense, last longer and cover a greater area, he said, “that really increases the concern we have for their impacts.”

Dr. Zhang is especially concerned about cities, which are often hotter than their surrounding areas because of the urban heat island effect . “If those heat waves last in the city for much longer than before, that would cause a very dangerous situation,” he said.

Alongside his atmospheric research, Dr. Zhang is helping with local efforts to plant more trees and grasses around bus stops in Salt Lake City, where people have to wait in the sun during increasingly hot summers. He suggested that cities build more cooling centers, especially for people experiencing homelessness.

“There are some things a community can do,” he said.

While waiting for international leaders to make progress on cutting greenhouse gas emissions and stopping climate change, Dr. Zhang said, local adaptation efforts are important to help keep people safer.

Delger Erdenesanaa is a reporter covering climate and the environment and a member of the 2023-24 Times Fellowship class, a program for journalists early in their careers. More about Delger Erdenesanaa

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Pulling together: how Cambridge came to dominate the Boat Race – a photo essay

The race along the River Thames between England’s two greatest universities spans 195 years of rivalry and is now one of the world’s oldest and most famous amateur sporting events. Our photographer has been spending time with the Cambridge University Boat Club over the past few months as they prepare for 2024’s races

T he idea of a Boat Race between the two universities dates back to 1829, sparked into life by a conversation between Old Harrovian schoolfriends Charles Merivale, a student at the time at St John’s College Cambridge, and Charles Wordsworth who was at Christ Church Oxford. On 12 March that year, following a meeting of the newly formed Cambridge University Boat Club, a letter was sent to Oxford.

The University of Cambridge hereby challenge the University of Oxford to row a match at or near London each in an eight-oar boat during the Easter vacation.

From then, the Cambridge University Boat Club has existed to win just one race against just one opponent, something Cambridge has got very good at recently. Last year the Light Blues won every race: the open-weight men’s and women’s races, both reserve races, plus both lightweight races – six victories, no losses, an unprecedented clean sweep. Cambridge women’s open-weight boat, or blue boat, has won the last six Boat Races while the men’s equivalent have won five out of the last seven. In such an unpredictable race, where external factors can play a large part, this dominance is startling.

Thames trials

Rough water as the two women’s boats make their way along the River Thames near Putney Embankment during the Cambridge University Boat Race trials.

It’s a mid-December day by the River Thames. The sky and water merge together in a uniform battleship grey and the bitter north wind whips the tops off the waves. Outside a Putney boathouse two groups of tense-looking women dressed in duck-egg blue tops and black leggings with festive antlers in their hair are huddling together, perhaps for warmth, maybe for solidarity. The odd nervous bout of laughter breaks out. For some of them this is about to be their first experience of rowing on the Tideway, a baptism of fire on the famous stretch of London water where the Boat Race takes place. “Perfect conditions,” remarks Paddy Ryan, the head coach for Cambridge University women, for this is trial eights day, when friends in different boats duel for coveted spots in the top boat.

A couple of hours later these women along with their male equivalents will have pushed themselves to the absolute limit, so much so that several of the men are seen trying to throw up over the side of their boats at the finish under Chiswick Bridge. This may be brutal but it’s just the start. For these students the next few months are going to be incredibly tough, balancing academic work with training like a professional athlete. Through the harshest months of the year they will be focused on preparing for the end of March and a very simple goal: beating Oxford in the Boat Race.

Agony for one of the men’s boats after the finish of the race near Chiswick Bridge during the Cambridge University Boat Race trials.

Ely early mornings

Two of the women’s boats head out in the early morning for a training session on the Great Ouse.

Early winter mornings on the banks of the Great Ouse, well before the sun has risen, can be pretty bleak. In the pitch black a batch of light blue minivans drop off the men and women rowers together at the sleek Ely boathouse that was opened in 2016 at the cost of £4.9m – it’s here that all Cambridge’s on-water training takes place. Very soon a fleet of boats carrying all the teams takes to the water for a training session that may last a couple of hours. Then it’s a quick change, a lift to the train station and back to Cambridge for morning lectures.

The women’s squad head into the Ely boathouse after a 6am drop-off.

As a rower descends the stairs to the bays where the boats are stored, there is a clear indication of why it was built and why they are there. “This is where we prepare to win Boat Races,” a sign says. Since this boathouse was built, Cambridge have won 30 of the 37 races across all categories.

Top: The men’s squad stretch in the boathouse before an early morning training session and a member of the men’s blue boat descends the stairs into where the boats are kept. Below: One of the men’s teams set off for early morning training and the women’s blue boat rows past the women’s lightweight crew during a training session.

It’s a far cry from the old tin sheds with barely any heating and no showers. These current facilities are impressive, enabling the entire men’s and women’s squads to be there at the same time and get boats out.

Top: The men’s blue boat prepare to derig their boat at their Ely training site. Above: The women’s blue boat put their vessel back in the boathouse after a training session on the Great Ouse.

But it’s not just the boathouse that has contributed so much, it’s also the stretch of water they train on. In a year when floods have affected so many parts of the country it has really come into its own. Paddy Ryan, the chief women’s coach, explains: “Along this stretch the river is actually higher than the surrounding land. The water levels are carefully managed by dikes and pumps. As a result we haven’t lost a single session to flooding. That’s not the case for Oxford. I believe their boathouse has been flooded multiple times this year, unable to get to their boats. We’ve had multiple storms but we’ve been able to row through them all.”

The men’s third boat practises on the Great Ouse.

It’s a flat, unforgiving landscape, especially in midwinter, definitely not the prettiest stretch of water, but Cambridge don’t care. Ryan says: “It might be a little dull on the viewing perspective but we could row on for 27km before needing to turn round. We have a 5km stretch that is marked out every 250m. We are lucky to have it.”

The men’s blue boat practise their starts on the long straight on the Great Ouse.

The sweat box

Members of the men’s squad check on their technique with the use of a mirror at the Goldie boathouse.

The old-fashioned Goldie boathouse is right in the centre of Cambridge perched on the banks of the River Cam. Built in 1873, its delicate exterior belies what goes on inside. This is the boat club’s pain cave, where the rowers sweat buckets, pushing themselves over and over again; it’s a good job the floor is rubberised and easy to wipe clean.

A wreath to Charles Merivale, the founder of the Boat Race, and wood panelling in the upstairs room at the Goldie boathouse which commemorates Cambridge crews that have competed in the Boat Race from 1829.

(Top) Seb Benzecry, men’s president of the Cambridge University Boat Club, and (above) Martin Amethier, a member of the reserve Goldie crew, sweat during sessions on ergo machines.

Iris Powell of the women’s blue boat (above) performs pull-ups during a training session.

Above left: Hannah Murphy, the cox of the women’s blue boat, urges on four of her crew (left to right) Gemma King, Megan Lee, Jenna Armstrong and Clare Hole, as they undertake a long session on the ergo machines. Above right: Kenny Coplan, a member of the men’s blue boat crew, looks exhausted then writes in his times after his session on an ergo machine (below).

Brutal sessions on the various ergo machines, where thousands of metres are clocked and recorded, are a staple of the training regime set in place. If there is any slacking off the students just need to look up at one of the walls where a map of the Boat Race course hangs. The “S” shape of the Thames has been carefully coloured in the correct shade of blue and record timings for various key points on the course have been written in for both men and women. All but one record, and that one is shared, is held by Cambridge.

Paddy Ryan, the women’s chief coach, talks to the women’s blue boat during a training session on the River Great Ouse in February.

A key ingredient in any successful team is the coaching. Cambridge’s setup is stable and well established. Paddy Ryan is the chief women’s coach, a genial, tall Australian, he has been part of the women’s coaching team since 2013. The care and devotion to his squad is perfectly clear. “I have my notebook next to my bed so I can jot things down. I wake up in the middle of the night going: am I making the right decisions? I care about them as people and I need to manage them … We joke as coaches that we are teaching some of the smartest people on the planet how to pull on a stick.”

Rob Baker, the chief men’s coach, has Cambridge rowing in the blood. Born and bred in the city, his father was a university boatman for 25 years. He even married into the sport – his wife, Hayley, rowed for Cambridge as a lightweight – so it was no surprise that he became part of the coaching setup way back in 2001. He was the first full-time women’s coach in 2015 then moved to take over the men in 2018.

Rob Baker, the men’s chief coach, talks to his blue boat at their Ely training site.

Apart from an obvious role in the development of rowing skills, a key part of their job is making sure there is a balance for their student athletes. They understand they have to juggle training needs. “Every week we have a general plan,” says Baker, “but then someone might have an extra class or supervision they’ve got to do so we have to move around it. They are studying at one of the most competitive universities in the world with the highest standards so you’ve got to give them space to do that properly.” He goes on: “But when they get on the start line for their race, they’ll be just as competitive as if they were professionals.”

The presidents

Jenna Armstrong and Seb Benzecry discuss their plans in the Great Hall at Jesus College.

Every year one man and one woman are elected presidents to represent Cambridge University Boat Club. They are the captains and leaders, not only responsible for helping design the training programme in conjunction with the coaches but also making budgetary and tactical decisions along the way. This year both of them, Jenna Armstrong and Seb Benzecry, are from the same college, Jesus, which helps the communication between the two of them. They share ideas and knowledge, thoughts and worries. Their lives, for these intense few months, are a juggling act.

Armstrong is a 30-year-old from New Jersey, and doing a PhD in physiology. Once a very keen competitive junior skier she was forced to abandon her hopes of a career on the slopes after a number of serious knee injuries. She only started rowing in 2011 and only became aware of the Boat Race when she saw it on TV a couple of years later.

Jenna Armstrong, cycling down the Chimney, the grand entrance to Jesus College, to go to the other side of the city to carry out more of her PhD research at the department of physiology, development and neuroscience.

The research she carries out at the university labs could be turn out to be life-saving. “I study mitochondrial function in placentas from women from all over the world to learn how genetic and environmental factors during pregnancy can influence placental metabolism and impact the health of both mother and baby. I’m particularly interested in growth restriction which affects about 10% of babies worldwide. That can have lifelong implications for these babies and currently we don’t have any treatment for this.”

Benzecry, 27, is studying for a PhD in film and screen studies, and comes from a completely different rowing background. He grew up just a stone’s throw from the Boat Race course and went to a school on the banks of the Thames. This will be his 14th year of competitive rowing but his fourth and last Boat Race.

“ I remember one year my birthday fell on race day and we watched after my birthday party. Because we live fairly close to the course, I’ve always felt connected to the race.”

Seb Benzecry stands next to an Antony Gormley statue in the Quincentenary Library at Jesus College as he conducts research for his dissertation which forms part of his PhD in film and screen studies.

Talking about how hard it is to get the right balance between academic student life and rowing, Benzecry says: “I guess you have to accept there are many, many things you can’t do, you just don’t have time for during the season. You have to put the blinkers on.”

Armstrong says: “I have to be very prepared, very strategic and organised. I pack everything the night before, and then once I leave my room in the morning, I don’t go back. That allows me to go to training, go to the lab, go to training again. It’s surreal actually, to come to a place like Cambridge, have one of the best educations in the world on top of the most incredible rowing experiences in the world. We have a thing now in the boat, when we are doing something incredibly hard, I say this is my ideal Saturday, I wouldn’t want to be anywhere else. I would rather be here than in bed or on a date. And I make everyone else say it with me too. I’d rather be nowhere else.”

Benzecry states: “When it’s really bad, when training is so hard, we say Oxford aren’t doing this, they could never do this. It’s an incredibly powerful thing to be thinking we work harder than them, our culture is better than them. They don’t want to go hard as we do – they might think they do but they don’t, they just don’t have it.”

Integration

The men’s and women’s blue boats during a training session on the River Great Ouse in February.

Until 1 August 2020, there were three separate university boat clubs in Cambridge: one for open-weight men, one for lightweight men, and one for open-weight and lightweight women. Since they merged to become one club, it has undoubtedly helped with everyone sharing the same resources and motivating and inspiring one another. No one is more important and everyone has a key part to play in the result. This year, Oxford have followed suit.

Baker says: “I definitely feel, for the athletes themselves, it makes a big difference. They all feel like they’re contributing to one common goal. Every cog in the wheel has to do its job but for sure it feels like one big team on a mission.”

Benzecry explains: “We’re seeing each other train, we’re all out on the water at the same time, we’re supporting each other throughout the season, building a sense of momentum for the whole club towards the races. Everyone’s just inspiring each other all the time and I think that’s been such a sort of cultural shift for Cambridge.”

The men’s blue boat pack their craft on to a trailer at their Ely training site ready for the trip down to London for the Boat Race.

Siobhan Cassidy, the chair of the Boat Race, knows from first-hand how the integration has helped. She rowed for the Light Blues in 1995 and had a key role in the transition. “We could see the advantages of working together, collaborating as a bigger team, the positive impact we felt that could have on performance. But not just the output, actually the whole experience for the young people taking part.”

Siobhan Cassidy, the chair of the Boat Race, pictured at the Thames Rowing Club at Putney Embankment.

This Saturday, if the weather holds, an estimated 250,000 people, the vast majority of whom have no allegiance to one shade of blue or the other, will pack the banks of the Thames to see these races. It’s one of the largest free events in Britain. Broadcast live on BBC One, the race is also beamed to 200 countries across the world.

The starting stone for the University Boat Race and pavement inscription: “The best leveller is the river we have in common” at Putney Embankment.

A map of the Boat Race course at the Goldie boathouse, with the Thames coloured in Cambridge blue and record timings written in for men and women showing almost total Cambridge dominance.

A sporting pinnacle being contested on a fast-flowing, unpredictable river by two teams of university students – it’s pretty bizarre. But maybe it’s that quirkiness that keeps the race, after almost two hundred years, still going strong. And even more bizarre to think that Cambridge, the current dominant force in the Boat Race, a sporting event that can’t shrug off its elitist stereotype, owes so much of that success to such egalitarian principles.

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Elon Musk Announces Significant Changes to X. Here’s What to Know

E lon Musk has announced new changes to social media platform X (formerly Twitter) that will allow certain accounts to unlock free premium features.

Posting on the platform Thursday, the 52-year old tech billionaire, and TIME’s 2021 Person of the Year , said: “Going forward, all X accounts with over 2500 verified subscriber followers will get Premium features for free and accounts with over 5000 will get Premium+ for free.”

Previously, X Premium features would cost a user $8 per month and include the ability to share longer posts and video uploads, have larger reply prioritization, and see fewer adverts on their timeline. Meanwhile X Premium+ users have all the features of Premium with no adverts in the For You and Following timelines, as well as access to generative artificial intelligence chatbot Grok . 

These models are the only way users can now display a blue checkmark that once denoted a verified account before the Tesla and SpaceX CEO acquired Twitter Inc for $44bn in April 2022.

The changes come two days after a U.S. judge threw out a lawsuit brought by X against a group that claimed hate speech has risen on the app since Musk’s takeover. 

X accused the Center for Countering Digital Hate (CCDH) of taking "unlawful" steps to access and misrepresent its company data in CCDH research. But Judge Charles Breyer said it was "evident" that Musk’s X could not handle criticism and that his firm was "punishing the defendants for their speech." X is set to appeal Judge Breyer’s decision. 

Read More: A Brief History of Elon Musk Saying One Thing and Doing Another at Twitter

The number of people using X each day is also reportedly falling, lagging behind its market rivals Instagram and TikTok. 

As of February, the platform’s daily usership in America has decreased by 23% since November 2022, the month after Musk’s takeover was finalized, according to Sensor Tower figures reported by NBC News . Meanwhile, figures show that TikTok recorded drops of 10%, while Facebook, Instagram, and Snapchat all had user slumps of less than 5%.

Outside the U.S., global figures have also taken a hit. Daily active users on X fell to 174 million, a decline of 15% on the previous year. Meanwhile Snapchat grew by 8.8%, Instagram recorded 5.3%, Facebook 1.5%, and TikTok 0.5%, per Sensor Tower data.  

“This decline in X mobile app active users may have been driven by user frustration over flagrant content, general platform technical issues, and the growing threat of short-form video platforms,” Abe Yousef, a senior insights analyst at Sensor Tower, wrote in a research report. 

In a post shared by X Data on March 18, the platform said it currently welcomes 250 million global daily users. Outlets such as NBC News have been quick to point out that this is still a decrease from the 258 million users Musk reported in 2022 , upon completing his acquisition of the platform.

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Write to Armani Syed at [email protected]

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