Abstract Prevailing theories of partisan incivility on social media suggest that it derives from disagreement about political issues or from status competition between groups. This study—which analyzes the commenting behavior of Reddit users across diverse cultural contexts (subreddits)—tests the alternative hypothesis that such incivility derives in large part from a selection effect: Toxic people are especially likely to opt into discourse in partisan contexts. First, we examined commenting behavior across over 9,000 unique cultural contexts (subreddits) and confirmed that discourse is indeed more toxic in partisan (e.g. r/progressive, r/conservatives) than in nonpartisan contexts (e.g. r/movies, r/programming). Next, we analyzed hundreds of millions of comments from over 6.3 million users and found robust evidence that: (i) the discourse of people whose behavior is especially toxic in partisan contexts is also especially toxic in nonpartisan contexts (i.e. people are not politics-only toxicity specialists); and (ii) when considering only nonpartisan contexts, the discourse of people who also comment in partisan contexts is more toxic than the discourse of people who do not. These effects were not driven by socialization processes whereby people overgeneralized toxic behavioral norms they had learned in partisan contexts. In contrast to speculation about the need for partisans to engage beyond their echo chambers, toxicity in nonpartisan contexts was higher among people who also comment in both left-wing and right-wing contexts (bilaterally engaged users) than among people who also comment in only left-wing or right-wing contexts (unilaterally engaged users). The discussion considers implications for democratic functioning and theories of polarization.
The social media discourse of engaged partisans is toxic even when politics are irrelevant
Abstract. Prevailing theories of partisan incivility on social media suggest that it derives from disagreement about political issues or from status compet
The social media discourse of engaged partisans is toxic even when politics are irrelevant
Abstract. Prevailing theories of partisan incivility on social media suggest that it derives from disagreement about political issues or from status compet
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.
Redesigning algorithms to intervene on social norm misperceptions during a national election
For the first time in history, civic discourse commonly occurs in digital environments in which algorithms influence exposure to social information1,2. It is increasingly important to understand whether and how these algorithms affect political discourse3–5. Here we built custom feed-ranking algorithms with full control over their features, and randomly assigned 2,000 participants to use them for 8 weeks (before and after the 2024 US presidential election). We tested whether an engagement-based algorithm (used on major social media platforms6,7) amplifies intergroup, moralized and emotional (IME) information in ways that skew perceptions of social norms around political dialogue5,8, and whether it increased engagement with IME content and perceptions of partisan animosity (compared with a reverse-chronological feed9,10). We also developed and tested a ‘diversified extremity’ algorithm to reduce the influence of extreme users11–13 to improve the accuracy of social norm perception14–16 and reduce perceptions of partisan animosity. We found that engagement-based feeds amplified IME and toxic content relative to reverse-chronological feeds, with the largest increases in moral outrage and political content. Engagement-based feeds also reduced prescriptive norm perception accuracy (albeit in an unexpected direction) and increased perceived partisan animosity. However, they did not significantly alter users’ own engagement behaviours. The diversified extremity algorithm reduced IME and toxic content exposure, improved prescriptive norm accuracy, yet maintained comparable platform enjoyment—suggesting that reducing the influence of extreme users can curb algorithmic distortions without diminishing user experience.
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.
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.
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.
Deliberation and Identity Rules: The Effect of Anonymity, Pseudonyms and Real-Name Requirements on the Cognitive Complexity of Online News Comments
How do identity rules influence online deliberation? We address this question by drawing on a data set of 45 million comments on news articles on the Huffington Post from January 2013 to May 2015. At the beginning of this period, the site allowed commenting under what we call non-durable pseudonyms. In December 2013, Huffington Post moved to regulate its forum by requiring users to authenticate their accounts. And in June 2014, Huffington Post outsourced commenting to Facebook altogether, approximating a ‘real-name’ environment. We find a significant increase in the cognitive complexity of comments (a proxy for one aspect of deliberative quality) during the middle phase, followed by a decrease following the shift to real-name commenting through Facebook. Our findings challenge the terms of the apparently simple trade-off between the goods and bads of anonymous and real-name environments and point to the potential value of durable pseudonymity in the context of online discussion.
Deliberation and Identity Rules: The Effect of Anonymity, Pseudonyms and Real-Name Requirements on the Cognitive Complexity of Online News Comments
How do identity rules influence online deliberation? We address this question by drawing on a data set of 45 million comments on news articles on the Huffington Post from January 2013 to May 2015. At the beginning of this period, the site allowed commenting under what we call non-durable pseudonyms. In December 2013, Huffington Post moved to regulate its forum by requiring users to authenticate their accounts. And in June 2014, Huffington Post outsourced commenting to Facebook altogether, approximating a ‘real-name’ environment. We find a significant increase in the cognitive complexity of comments (a proxy for one aspect of deliberative quality) during the middle phase, followed by a decrease following the shift to real-name commenting through Facebook. Our findings challenge the terms of the apparently simple trade-off between the goods and bads of anonymous and real-name environments and point to the potential value of durable pseudonymity in the context of online discussion.
Misplaced Divides? Discussing Political Disagreement With Strangers Can Be Unexpectedly Positive
Differences of opinion between people are common in everyday life, but discussing those differences openly in conversation may be unnecessarily rare. We report three experiments ( N = 1,264 U.S.-based adults) demonstrating that people’s interest in discussing important but potentially divisive topics is guided by their expectations about how positively the conversation will unfold, leaving them more interested in having a conversation with someone who agrees versus disagrees with them. People’s expectations about their conversations, however, were systematically miscalibrated such that people underestimated how positive these conversations would be—especially in cases of disagreement. Miscalibrated expectations stemmed from underestimating the degree of common ground that would emerge in conversation and from failing to appreciate the power of social forces in conversation that create social connection. Misunderstanding the outcomes of conversation could lead people to avoid discussing disagreements more often, creating a misplaced barrier to learning, social connection, free inquiry, and free expression.
The Noisy Room
Only 3% of social media users post toxic content — but Americans estimate 43%. An interactive essay.
Digital Social Norm Enforcement: Online Firestorms in Social Media
Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.
Digital Social Norm Enforcement: Online Firestorms in Social Media
Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.
Social media discourse is driven by false #polarization We find that: 1) ideologues post opinions more than moderates 2) extreme attitudes are posted more than moderate one 3) This replicates in 40 countries 4) Opinions that users post are more hostile than the beliefs they keep to themselves
Is social media truly making us more politically polarized, or does it simply over-represent our most extreme opinions? In our new preprint, @small-schulz.bsky.social and I present *punishing evidence* that social media systematically overrepresents users’ most extreme political opinions. 1/n
