There is widespread concern that foreign actors are using social media to interfere in elections worldwide. Yet data have been unavailable to investigate links between exposure to foreign influence campaigns and political behavior. Using longitudinal survey data from US respondents linked to their Twitter feeds, we quantify the relationship between exposure to the Russian foreign influence campaign and attitudes and voting behavior in the 2016 US election. We demonstrate, first, that exposure to Russian disinformation accounts was heavily concentrated: only 1% of users accounted for 70% of exposures. Second, exposure was concentrated among users who strongly identified as Republicans. Third, exposure to the Russian influence campaign was eclipsed by content from domestic news media and politicians. Finally, we find no evidence of a meaningful relationship between exposure to the Russian foreign influence campaign and changes in attitudes, polarization, or voting behavior. The results have implications for understanding the limits of election interference campaigns on social media.
Exposure to the Russian Internet Research Agency foreign influence campaign on Twitter in the 2016 US election and its relationship to attitudes and voting behavior
There is widespread concern that foreign actors are using social media to interfere in elections worldwide. Yet data have been unavailable to investigate links between exposure to foreign influence campaigns and political behavior. Using longitudinal survey data from US respondents linked to their Twitter feeds, we quantify the relationship between exposure to the Russian foreign influence campaign and attitudes and voting behavior in the 2016 US election. We demonstrate, first, that exposure to Russian disinformation accounts was heavily concentrated: only 1% of users accounted for 70% of exposures. Second, exposure was concentrated among users who strongly identified as Republicans. Third, exposure to the Russian influence campaign was eclipsed by content from domestic news media and politicians. Finally, we find no evidence of a meaningful relationship between exposure to the Russian foreign influence campaign and changes in attitudes, polarization, or voting behavior. The results have implications for understanding the limits of election interference campaigns on social media.
Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election
Approximately 50% of tweets in X's user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: What political content are users exposed to beyond their established networks, and what implications does this have for democratic discourse online? In this paper, we present a six-week audit of X's algorithmic content recommendations during the 2024 U.S. Presidential Election by deploying 120 sock-puppet monitoring accounts to capture tweets from their personalized "For You" timelines. Our objective is to quantify out-of-network content exposure for right- and left-leaning user profiles and assess any potential inequalities and biases in political exposure. Our findings indicate that X's algorithm skews exposure toward a few high-popularity accounts across all users, with right-leaning users experiencing the highest level of exposure inequality. Both left- and right-leaning users encounter amplified exposure to accounts aligned with their own political views and reduced exposure to opposing viewpoints. Additionally, we observe that new accounts experience a right-leaning bias in exposure within their default timelines. Our work contributes to understanding how content recommendation systems may induce and reinforce biases while exacerbating vulnerabilities among politically polarized user groups. We underscore the importance of transparency-aware algorithms in addressing critical issues such as safeguarding election integrity and fostering a more informed digital public sphere.
How do social media feed algorithms affect attitudes and behavior in an election campaign?
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.
The political effects of X’s feed algorithm
Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects1. Here we present results from a 2023 field experiment on Elon Musk’s platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users’ feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X’s algorithm has persistent effects on users’ current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship.
The political effects of X’s feed algorithm
Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects1. Here we present results from a 2023 field experiment on Elon Musk’s platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users’ feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X’s algorithm has persistent effects on users’ current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship.
Russian disinformation campaign sets sights on US midterms
A Russian disinformation operation has turned its attention to the US midterm elections, unleashing a series of deepfake videos that depict celebrities attacking Democrats, researchers said, with more…
The small effects of political advertising are small regardless of context, message, sender, or receiver: Evidence from 59 real-time randomized experiments
Campaign persuasion operates with notable consistency across political parties, message tone, and information environment., Evidence across social science indicates that average effects of persuasive messages are small. One commonly offered explanation for these small effects is heterogeneity: Persuasion may only work well in specific circumstances. To evaluate heterogeneity, we repeated an experiment weekly in real time using 2016 U.S. presidential election campaign advertisements. We tested 49 political advertisements in 59 unique experiments on 34,000 people. We investigate heterogeneous effects by sender (candidates or groups), receiver (subject partisanship), content (attack or promotional), and context (battleground versus non-battleground, primary versus general election, and early versus late). We find small average effects on candidate favorability and vote. These small effects, however, do not mask substantial heterogeneity even where theory from political science suggests that we should find it. During the primary and general election, in battleground states, for Democrats, Republicans, and Independents, effects are similarly small. Heterogeneity with large offsetting effects is not the source of small average effects.
On Digital Disinformation and Democratic Myths – MediaWell
For two years, from 2015 to 2017, the Russian Internet Research Agency (IRA) operated a “Blacktivist” Facebook page. The Blacktivist account posed as an organic, grassroots online activism site, amplifying and contributing to the Black Lives Matter movement. It shared news, memes, and perspectives about racial injustice in America. Blacktivist was the most prominent of the IRA-generated Facebook pages, with over 6.18 million “interactions” recorded across its top 500 posts. The page had 360,000 likes, more than the verified Black Lives Matter account on Facebook (O’Sullivan and Byers 2017). In congressional hearings about Russian online disinformation and propaganda campaigns, poster-sized versions of Blacktivist posts and memes were on prominent display. Blacktivist stands as a warning sign of the sheer volume of online disinformation activities by foreign actors in US politics.
When Do Parties Lie? Misinformation and Radical-Right Populism Across 26 Countries
The spread of misinformation has emerged as a global concern. Academic attention has recently shifted to emphasize the role of political elites as drivers of misinformation. Yet, little is known of the relationship between party politics and the spread of misinformation—in part due to a dearth of cross-national empirical data needed for comparative study. This article examines which parties are more likely to spread misinformation, by drawing on a comprehensive database of 32M tweets from parliamentarians in 26 countries, spanning 6 years and several election periods. The dataset is combined with external databases such as Parlgov and V-Dem, linking the spread of misinformation to detailed information about political parties and cabinets, thus enabling a comparative politics approach to misinformation. Using multilevel analysis with random country intercepts, we find that radical-right populism is the strongest determinant for the propensity to spread misinformation. Populism, left-wing populism, and right-wing politics are not linked to the spread of misinformation. These results suggest that political misinformation should be understood as part and parcel of the current wave of radical right populism, and its opposition to liberal democratic institution.
Shifts in U.S. Social Media Use, 2020–2024: Decline, Fragmentation, and Enduring Polarization
Using nationally representative data from the 2020 and 2024 American National Election Studies (ANES), this paper describes how U.S. social media use has shifted across platforms, demographics, and politics. Overall platform reach declined, driven by growth in the share of Americans — especially the youngest and oldest cohorts — who report using no social media. Visiting and posting activity on Twitter/X and Facebook have fallen by nearly 50% since 2020, with the decline on Twitter/X driven primarily by reduced participation among Democratic users. While Facebook, YouTube, and Twitter/X lost ground, TikTok and Reddit grew modestly, consistent with a more fragmented digital public sphere. Platform audiences aged and became slightly more educated and racially diverse. Politically, most platforms shifted toward Republican users while remaining, on balance, Democratic-leaning. Twitter/X experienced the largest change: among posters, the partisan balance swung over 70 percentage points from Democrats to Republicans. Across platforms, political posting remains closely tied to affective polarization, as the most partisan respondents are also the most active. As casual users disengage while polarized partisans remain vocal, online discourse becomes narrower and more ideologically extreme.
Sharing without clicking on news in social media
Social media have enabled laypersons to disseminate, at scale, links to news and public affairs information. Many individuals share such links without first reading the linked information. Here we analysed over 35 million public Facebook posts with uniform resource locators shared between 2017 and 2020, and discovered that such ‘shares without clicks’ (SwoCs) constitute around 75% of forwarded links. Extreme and user-aligned political content received more SwoCs, with partisans engaging in it more than politically neutral users. In addition, analyses with 2,969 false uniform resource locators revealed higher shares and, hence, SwoCs by conservatives (76.94%) than liberals (14.25%), probably because, in our dataset, the vast majority (76–82%) of them originated from conservative news domains. Findings suggest that the virality of political content on social media (including misinformation) is driven by superficial processing of headlines and blurbs rather than systematic processing of core content, which has design implications for promoting deliberate discourse in the online public sphere.
Political disinformation concentrates in repeated claims across media
The prevalence and dynamics of disinformation remain contentious, partly because measures have relied on posts containing URLs from untrustworthy sources. We move beyond this by detecting disinformation in text and across posts. Using retrieval-augmented generation, we match six million Facebook, Instagram, and X posts by politicians from the United States, United Kingdom, and Germany, and two million articles from news outlets to fact-checked claims. We show that disinformation is concentrated in a small number of combinations of policy issues and parties and that just over hundred distinct claims account for around half of all disinformation across the three countries. They often appear first on a single platform, later on multiple, and subsequently in news coverage before being fact-checked. These findings suggest that disinformation is concentrated in a narrow segment of political communication, potentially creating repeated exposure that may shape the terms of public debate through both social and news media.
The Role of Media in Political Polarization| Inoculation Can Reduce the Perceived Reliability of Polarizing Social Media Content
Little research is available on psychological interventions that counter susceptibility to polarizing online content. We conducted 3 studies (n1 = 472, n2 = 193, n3 = 772) to evaluate whether psychological resistance against polarizing social media content can be conferred, using the Bad News game, a “technique-based inoculation” intervention that simulates a social media feed. We investigate (1) whether technique-based inoculation can reduce susceptibility to content designed to fuel intergroup polarization; (2) whether technique-based inoculation can offer cross-protection against misinformation techniques that people were not inoculated against; and (3) whether political ideology plays a role in how people engage with anti-misinformation interventions. In Studies 1 and 3 (but not Study 2), we found that technique-based inoculation significantly reduces the perceived reliability of polarizing content and offers partial cross-protection against untreated misinformation techniques. We found no effect for attitudinal certainty and news-sharing intentions. Finally, we report preliminary evidence that people may choose to engage with politically congruent news topics within the intervention.
Reranking partisan animosity in algorithmic social media feeds alters affective polarization
Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by more than 2 points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings. , Editor’s summary Social media platforms’ opaque feed-ranking algorithms, which are designed to maximize engagement, may contribute to political polarization, body image, mental health, and other social issues. However, the evidence has rarely enabled conclusions about causality because external researchers need platforms’ permission to experimentally intervene. Collaborations with platforms also involve trade-offs that undermine researchers’ intellectual autonomy. Piccardi et al . circumvented this problem with a browser extension that intercepted feeds on X/Twitter in real time using a large language model (LLM) (see the Perspective by Allen and Tucker). Liberals and conservatives were randomly assigned to conditions where the LLM re-ranked feeds to up-rank or down-rank the visibility of hostile political content. Up-ranking increased political polarization, whereas down-ranking decreased it. This LLM tool enabled independent scholars to pull back the curtain on unresolved research questions without the platform’s permission. —Ekeoma Uzogara , INTRODUCTION Social media algorithms profoundly impact our lives, yet our understanding of these algorithms’ impact has been limited to interventions that the platforms themselves are willing to test and publish. In this article, we demonstrate a method for independent algorithmic reranking experiments in naturalistic settings and at scale. We leverage this method to demonstrate that algorithmic ranking can both raise and lower levels of affective political polarization in the United States, with no detectable differences across party lines. RATIONALE We built a browser extension that intercepted and reranked web-based social media feeds in real time. The extension used a large language model (LLM) to score content and then applied these scores to reorder the feed, all without requiring platform cooperation. Our approach identified content expressing antidemocratic attitudes and partisan animosity (AAPA) and controlled users’ exposure to such content through reranking. We conducted a preregistered field experiment with 1256 participants on X, the most widely used social media platform for political discourse in the United States, during the weeks leading up to the 2024 presidential election. Participants were randomly assigned to two parallel experiments in which their feeds were dynamically reranked for 1 week to either up- or down-rank AAPA posts. We measured the effects of these interventions on affective polarization (participants’ feelings toward the political outgroup) and emotional experience (anger, sadness, excitement, or calm) by using both in-feed and post-experiment surveys. Results were compared to those of participants in control conditions whose feeds were not reranked. RESULTS Reranking to change levels of exposure to AAPA content significantly influenced affective polarization. Increased AAPA exposure led to colder feelings toward the political outgroup, whereas decreased AAPA exposure led to warmer feelings. Both shifts amounted to more than 2 degrees on a 0- to 100- degree “feeling thermometer.” The impact on affective polarization was consistently detected in both in-feed and post-experiment surveys, with no evidence of partisan asymmetry. Changes to the feed algorithm caused in-feed but not post-experiment changes to participants’ negative emotions. CONCLUSION This work demonstrates a new method for reranking social media feeds that enables testing interventions without platform collaboration. We found that increasing exposure to AAPA posts significantly increases affective polarization and negative emotions, whereas decreasing exposure reduces them. These changes were comparable in size to 3 years of change in United States affective polarization. As political polarization and societal division become increasingly linked to social media activity, our findings provide a potential pathway for platforms to address these challenges through algorithmic interventions. Together, these interventions may result in algorithms that not only reduce partisan animosity but also promote greater social trust and healthier democratic discourse across party lines. A platform-independent field experiment demonstrates that reranking content expressing antidemocratic attitudes and partisan animosity in social media feeds alters affective polarization. A browser extension reranked participants’ social media feeds by reducing or increasing exposure to posts expressing AAPA, which in turn led to corresponding decreases or increases in affective polarization.
Reranking partisan animosity in algorithmic social media feeds alters affective polarization
Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by more than 2 points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings. , Editor’s summary Social media platforms’ opaque feed-ranking algorithms, which are designed to maximize engagement, may contribute to political polarization, body image, mental health, and other social issues. However, the evidence has rarely enabled conclusions about causality because external researchers need platforms’ permission to experimentally intervene. Collaborations with platforms also involve trade-offs that undermine researchers’ intellectual autonomy. Piccardi et al . circumvented this problem with a browser extension that intercepted feeds on X/Twitter in real time using a large language model (LLM) (see the Perspective by Allen and Tucker). Liberals and conservatives were randomly assigned to conditions where the LLM re-ranked feeds to up-rank or down-rank the visibility of hostile political content. Up-ranking increased political polarization, whereas down-ranking decreased it. This LLM tool enabled independent scholars to pull back the curtain on unresolved research questions without the platform’s permission. —Ekeoma Uzogara , INTRODUCTION Social media algorithms profoundly impact our lives, yet our understanding of these algorithms’ impact has been limited to interventions that the platforms themselves are willing to test and publish. In this article, we demonstrate a method for independent algorithmic reranking experiments in naturalistic settings and at scale. We leverage this method to demonstrate that algorithmic ranking can both raise and lower levels of affective political polarization in the United States, with no detectable differences across party lines. RATIONALE We built a browser extension that intercepted and reranked web-based social media feeds in real time. The extension used a large language model (LLM) to score content and then applied these scores to reorder the feed, all without requiring platform cooperation. Our approach identified content expressing antidemocratic attitudes and partisan animosity (AAPA) and controlled users’ exposure to such content through reranking. We conducted a preregistered field experiment with 1256 participants on X, the most widely used social media platform for political discourse in the United States, during the weeks leading up to the 2024 presidential election. Participants were randomly assigned to two parallel experiments in which their feeds were dynamically reranked for 1 week to either up- or down-rank AAPA posts. We measured the effects of these interventions on affective polarization (participants’ feelings toward the political outgroup) and emotional experience (anger, sadness, excitement, or calm) by using both in-feed and post-experiment surveys. Results were compared to those of participants in control conditions whose feeds were not reranked. RESULTS Reranking to change levels of exposure to AAPA content significantly influenced affective polarization. Increased AAPA exposure led to colder feelings toward the political outgroup, whereas decreased AAPA exposure led to warmer feelings. Both shifts amounted to more than 2 degrees on a 0- to 100- degree “feeling thermometer.” The impact on affective polarization was consistently detected in both in-feed and post-experiment surveys, with no evidence of partisan asymmetry. Changes to the feed algorithm caused in-feed but not post-experiment changes to participants’ negative emotions. CONCLUSION This work demonstrates a new method for reranking social media feeds that enables testing interventions without platform collaboration. We found that increasing exposure to AAPA posts significantly increases affective polarization and negative emotions, whereas decreasing exposure reduces them. These changes were comparable in size to 3 years of change in United States affective polarization. As political polarization and societal division become increasingly linked to social media activity, our findings provide a potential pathway for platforms to address these challenges through algorithmic interventions. Together, these interventions may result in algorithms that not only reduce partisan animosity but also promote greater social trust and healthier democratic discourse across party lines. A platform-independent field experiment demonstrates that reranking content expressing antidemocratic attitudes and partisan animosity in social media feeds alters affective polarization. A browser extension reranked participants’ social media feeds by reducing or increasing exposure to posts expressing AAPA, which in turn led to corresponding decreases or increases in affective polarization.
Why Don’t We Learn from Social Media? Studying Effects of and Mechanisms behind Social Media News Use on General Surveillance Political Knowledge
Does exposure to news affect what people know about politics? This old question attracted new scholarly interest as the political information environment is changing rapidly. In particular, since c...
