Academic Research

The question of how social media usage impacts societal polarization continues to generate great interest among both the research community and broader public. Nevertheless, there are still very few rigorous empirical studies of the causal impact of social media usage on polarization. To explore this question, we replicate the only published study to date that tests the effects of social media cessation on interethnic attitudes (Asimovic et al., 2021). In a study situated in Bosnia and Herzegovina, the authors found that deactivating from Facebook for a week around genocide commemoration in Bosnia and Herzegovina had a negative effect on users’ attitudes toward ethnic outgroups, with the negative effect driven by users with more ethnically homogenous offline networks. Does this finding extend to other settings? In a pre-registered replication study, we implement the same research design in a different ethnically polarized setting: Cyprus. We are not able to replicate the main effect found in Asimovic et al. (2021): in Cyprus, we cannot reject the null hypothesis of no effect. We do, however, find a significant interaction between the heterogeneity of users’ offline networks and the deactivation treatment within our 2021 subsample, consistent with the pattern from Bosnia and Herzegovina. We also find support for recent findings (Allcott et al., 2020; Asimovic et al., 2021) that Facebook deactivation leads to a reduction in anxiety levels and suggestive evidence of a reduction in knowledge of current news, though the latter is again limited to our 2021 subsample.

Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.

We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users’ on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.

We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.

Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increases as we shift from potential exposure to actual exposure to engagement; (ii) there is an asymmetry between conservative and liberal audiences, with a substantial corner of the news ecosystem consumed exclusively by conservatives; and (iii) most misinformation, as identified by Meta’s Third-Party Fact-Checking Program, exists within this homogeneously conservative corner, which has no equivalent on the liberal side. Sources favored by conservative audiences were more prevalent on Facebook’s news ecosystem than those favored by liberals.

This paper demonstrates the use of differentially private hyperlink-level engagement data for measuring ideologies of audiences for web domains, individual links, or aggregations thereof. We examine a simple metric for measuring this ideological position and assess the conditions under which the metric is robust to injected, privacy-preserving noise. This assessment provides insights into and constraints on the level of activity one should observe when applying this metric to privacy-protected data. Grounding this work is a massive dataset of social media engagement activity where privacy-preserving noise has been injected into the activity data, provided by Facebook and the Social Science One (SS1) consortium. Using this dataset, we validate our ideology measures by comparing to similar, published work on sharing-based, homophily- and content-oriented measures, where we show consistently high correlation (>0.87). We then apply this metric to individual links from several popular news domains and demonstrate how one can assess link-level distributions of ideological audiences. We further show this estimator is robust to selection of engagement types besides sharing, where domain-level audience-ideology assessments based on views and likes show no significant difference compared to sharing-based estimates. Estimates of partisanship, however, suggest the viewing audience is more moderate than the audiences who share and like these domains. Beyond providing thresholds on sufficient activity for measuring audience ideology and comparing three types of engagement, this analysis provides a blueprint for ensuring robustness of future work to differential privacy protections.

The intersection of social media and politics is yet another realm in which Computational Social Science has a paramount role to play. In this review, I examine the questions that computational social scientists are attempting to answer – as well as the tools and methods they are developing to do so – in three areas where the rise of social media has led to concerns about the quality of democracy in the digital information era: online hate; misinformation; and foreign influence campaigns. I begin, however, by considering a precursor of these topics – and also a potential hope for social media to be able to positively impact the quality of democracy – by exploring attempts to measure public opinion online using Computational Social Science methods. In all four areas, computational social scientists have made great strides in providing information to policy makers and the public regarding the evolution of these very complex phenomena but in all cases could do more to inform public policy with better access to the necessary data; this point is discussed in more detail in the conclusion of the review.

Social media is used by millions of Americans to acquire political news and information. Most of this research has focused on understanding the way social media consumption affects the political behavior and preferences of White Americans. Much less is known about Latinos’ political activity on social media, who are not only the largest racial/ethnic minority group in the U.S., but they also continue to exhibit diverse political preferences. Moreover, about 30% of Latinos rely primarily on Spanish-language news sources (Spanish-dominant Latinos) and another 30% are bilingual. Given that Spanish-language social media is not as heavily monitored for misinformation than its English-language counterparts (Valencia, 2021; Paul, 2021), Spanish-dominant Latinos who rely on social media for news may be more susceptible to political misinformation than those Latinos who are exposed to English-language social media. We address this contention by fielding an original study that sampled a large number of Latino and White respondents. Consistent with our expectations, Latinos who rely on Spanish-language social media are more likely to believe in election fraud than those who use both English and Spanish social media new sources. We also find that Latinos engage in more political activities on social media when compared to White Americans, particularly on their social media of choice, WhatsApp.

To mitigate the spread of false news, researchers need to understand who visits low-quality news sites, what brings people to those sites, and what content they prefer to consume. Due to challenges in observing most direct website traffic, existing research primarily relies on alternative data sources, such as engagement signals from social media posts. However, such signals are at best only proxies for actual website visits. During an audit of far-right news websites, we discovered that The Gateway Pundit (TGP) has made its web traffic data publicly available, giving us a rare opportunity to understand what news pages people actually visit. We collected 68 million web traffic visits to the site over a one-month period and analyzed how people consume news via multiple features. Our referral analysis shows that search engines and social media platforms are the main drivers of traffic; our geo-location analysis reveals that TGP is more popular in counties where more people voted for Trump in 2020. In terms of content, topics related to 2020 US presidential election and 2021 US capital riot have the highest average number of visits. We also use these data to quantify to what degree social media engagement signals correlate with actual web visit counts. To do so, we collect Facebook and Twitter posts with URLs from TGP during the same time period. We show that all engagement signals positively correlate with web visit counts, but with varying correlation strengths. For example, total interaction on Facebook correlates better than Twitter retweet count. Our insights can also help researchers choose the right metrics when they measure the impact of news URLs on social media.

Increasing levels of political animosity in the United States invite speculation about whether polarization extends to aspects of daily life. However, empirical study about the relationship between political ideologies and lifestyle choices is limited by a lack of comprehensive data. In this research, we combine survey and Facebook Page “likes” data from more than 1,200 respondents to investigate the extent of polarization in lifestyle domains. Our results indicate that polarization is present in page categories that are somewhat related to politics – such as opinion leaders, partisan news sources, and topics related to identity and religion – but, perhaps surprisingly, it is mostly not evident in other domains, including sports, food, and music. On the individual level, we find that people who are higher in political news interest and have stronger ideological predispositions have a greater tendency to “like” ideologically homogeneous pages across categories. Our evidence, drawn from rare digital trace data covering more than 5,000 pages, adds nuance to the narrative of widespread polarization across lifestyle sectors and it suggests domains in which cross-cutting preferences are still observed in American life.

We analyze the spread of Donald Trump’s tweets that were flagged by Twitter using two intervention strategies—attaching a warning label and blocking engagement with the tweet entirely. We find that while blocking engagement on certain tweets limited their diffusion, messages we examined with warning labels spread further on Twitter than those without labels. Additionally, the messages that had been blocked on Twitter remained popular on Facebook, Instagram, and Reddit, being posted more often and garnering more visibility than messages that had either been labeled by Twitter or received no intervention at all. Taken together, our results emphasize the importance of considering content moderation at the ecosystem level.

Despite the belief that social media is altering intergroup dynamics—bringing people closer or further alienating them from one another—the impact of social media on interethnic attitudes has yet to be rigorously evaluated, especially within areas with tenuous interethnic relations. We report results from a randomized controlled trial in Bosnia and Herzegovina (BiH), exploring the effects of exposure to social media during 1 wk around genocide remembrance in July 2019 on a set of interethnic attitudes of Facebook users. We find evidence that, counter to preregistered expectations, people who deactivated their Facebook profiles report lower regard for ethnic outgroups than those who remained active. Moreover, we present additional evidence suggesting that this effect is likely conditional on the level of ethnic heterogeneity of respondents’ residence. We also extend the analysis to include measures of subjective well-being and knowledge of news. Here, we find that Facebook deactivation leads to suggestive improvements in subjective wellbeing and a decrease in knowledge of current events, replicating results from recent research in the United States in a very different context, thus increasing our confidence in the generalizability of these effects.

In this study, we analyze for the first time newly available engagement data covering millions of web links shared on Facebook to describe how and by which categories of U.S. users different types of news are seen and shared on the platform. We focus on articles from low-credibility news publishers, credible news sources, purveyors of clickbait, and news specifically about politics, which we identify through a combination of curated lists and supervised classifiers. Our results support recent findings that more fake news is shared by older users and conservatives and that both viewing and sharing patterns suggest a preference for ideologically congenial misinformation. We also find that fake news articles related to politics are more popular among older Americans than other types, while the youngest users share relatively more articles with clickbait headlines. Across the platform, however, articles from credible news sources are shared over 5.5 times more often and viewed over 7.5 times more often than articles from low-credibility sources. These findings offer important context for researchers studying the spread and consumption of information — including misinformation — on social media.

For nearly a century social scientists have sought to understand left–right ideological differences in values, motives, and thinking styles. Much progress has been made, but — as in other areas of research — this work has been criticized for relying on small and statistically unrepresentative samples and the use of reactive, self-report measures that lack ecological validity. In an effort to overcome these limitations, we employed automated text analytic methods to investigate the spontaneous, naturally occurring use of language in nearly 25,000 Twitter users. We derived 27 hypotheses from the literature on political psychology and tested them using 32 individual dictionaries. In 23 cases, we observed significant differences in the linguistic styles of liberals and conservatives. For instance, liberals used more language that conveyed benevolence, whereas conservatives used more language pertaining to threat, power, tradition, resistance to change, certainty, security, anger, anxiety, and negative emotion in general. In 17 cases, there were also significant effects of ideological extremity. For instance, moderates used more benevolent language, whereas extremists used more language pertaining to inhibition, tentativeness, affiliation, resistance to change, certainty, security, anger, anxiety, negative affect, swear words, and death-related language. These research methods, which are easily adaptable, open up new and unprecedented opportunities for conducting unobtrusive research in psycholinguistics and political psychology with large and diverse samples.

So-called “fake news” has renewed concerns about the prevalence and effects of misinformation in political campaigns. Given the potential for widespread dissemination of this material, we examine the individual-level characteristics associated with sharing false articles during the 2016 U.S. presidential campaign. To do so, we uniquely link an original survey with respondents’ sharing activity as recorded in Facebook profile data. First and foremost, we find that sharing this content was a relatively rare activity. Conservatives were more likely to share articles from fake news domains, which in 2016 were largely pro-Trump in orientation, than liberals or moderates. We also find a strong age effect, which persists after controlling for partisanship and ideology: On average, users over 65 shared nearly seven times as many articles from fake news domains as the youngest age group.

How accurate are survey-based measures of social media use, in particular about political topics? We answer this question by linking original survey data collected during the U.S. 2016 election campaign with respondents’ observed social media activity. We use supervised machine learning to classify whether these Twitter and Facebook account data are content related to politics. We then benchmark our survey measures on frequency of posting about politics and the number of political figures followed. We find that, on average, our self-reported survey measures tend to correlate with observed social media activity. At the same time, we also find a worrying amount of individual-level discrepancy and problems related to extreme outliers. Our recommendations are twofold. The first is for survey questions about social media use to provide respondents with options covering a wider range of activity, especially in the long tail. The second is for survey questions to include specific content and anchors defining what it means for a post to be “about politics.”

It is often claimed that social media platforms such as Facebook and Twitter are profoundly shaping political participation, especially when it comes to protest behavior. Whether or not this is the case, the analysis of “Big Data” generated by social media usage offers unprecedented opportunities to observe complex, dynamic effects associated with large-scale collective action and social movements. In this article, we summarize evidence from studies of protest movements in the United States, Spain, Turkey, and Ukraine demonstrating that: (1) Social media platforms facilitate the exchange of information that is vital to the coordination of protest activities, such as news about transportation, turnout, police presence, violence, medical services, and legal support; (2) in addition, social media platforms facilitate the exchange of emotional and motivational contents in support of and opposition to protest activity, including messages emphasizing anger, social identification, group efficacy, and concerns about fairness, justice, and deprivation as well as explicitly ideological themes; and (3) structural characteristics of online social networks, which may differ as a function of political ideology, have important implications for information exposure and the success or failure of organizational efforts. Next, we issue a brief call for future research on a topic that is understudied but fundamental to appreciating the role of social media in facilitating political participation, namely friendship. In closing, we liken the situation confronted by researchers who are harvesting vast quantities of social media data to that of systems biologists in the early days of genome sequencing.

As more than a billion people had done previously, on November 21, 2013, Ukrainian journalist and activist Mustafa Nayem wrote a Facebook post; this post, however, would have a much larger impact on subsequent political developments than most that had preceded it. Frustrated with President Viktor Yanukovych’s decision not to sign a long-promised association agreement with the European Union, Nayem asked others who shared his frustration to comment on his post. Even more importantly, Nayem wrote that if the post received at least 1,000 comments from people willing to join him, they should all go to Independence Square to protest. And indeed they did: starting with just a few thousand people, the protests would swell to be the largest since Ukraine’s independence, particularly after police used force against protesters at the end of November 2013. Eventually, these protests led to the resignation of the government, the exile of the former president, and indirectly to the secession of Crimea and the ongoing conflict in the eastern part of the country.