There is widespread concern that rising affective polarization—particularly dislike for outpartisans—exacerbates Americans’ anti-democratic attitudes. Accordingly, scholars and practitioners alike have invested great effort in developing depolarization interventions that reduce affective polarization. Critically, however, it remains unclear whether these interventions reduce anti-democratic attitudes, or only change sentiments towards outpartisans. Here we address this question with experimental tests (total n = 8,385) of three previously established depolarization interventions: correcting misperceptions of outpartisans, priming inter-partisan friendships and observing warm cross-partisan interactions between political leaders. While these depolarization interventions reliably reduced affective polarization, we do not find compelling evidence that these interventions reduced support for undemocratic candidates, support for partisan violence or prioritizing partisan ends over democratic means. Thus, future efforts to strengthen pro-democratic attitudes may do better if they target these outcomes directly. More broadly, these findings call into question the previously assumed causal effect of affective polarization on anti-democratic attitudes.
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.
Partidismo negativo, derecha radical y voto: un análisis de su relación en el contexto de las elecciones generales españolas de 2023
Although the concept of partisan identification included a positive and a negative dimension as postulated by the Michigan model, the truth is that the latter dimension has received practically no attention. The present research aims to evaluate to what extent negative partisanship currently represents an explanatory factor of electoral behavior in Spain. Specifically, we study how, in the context of the Spanish general elections of July 2023, negative partisanship towards the radical right increased the probability of voting for the PSOE and whether, in turn, the rejection generated by the PSOE also promoted support for Vox. To test these hypotheses, we use data from the III National Survey of Political Polarization in Spain, elaborated by the Murcian Centre for Public Opinion Studies (CEMOP) in the middle of the election campaign. The results of the binary logistic regression models implemented show that negative partisanship towards the radical right represents a (novel) explanatory factor of the vote for the PSOE, but that Vox did not feed significantly on the rejection of the socialists —nor even of Sumar—.We also note the exceptional capacity of the radical right to arouse processes of negative identification, especially among women.
The Minimal Persuasive Effects of Campaign Contact in General Elections: Evidence from 49 Field Experiments
Significant theories of democratic accountability hinge on how political campaigns affect Americans’ candidate choices. We argue that the best estimate of the effects of campaign contact and advertising on Americans’ candidates choices in general elections is zero. First, a systematic meta-analysis of 40 field experiments estimates an average effect of zero in general elections. Second, we present nine original field experiments that increase the statistical evidence in the literature about the persuasive effects of personal contact tenfold. These experiments’ average effect is also zero. In both existing and our original experiments, persuasive effects only appear to emerge in two rare circumstances. First, when candidates take unusually unpopular positions and campaigns invest unusually heavily in identifying persuadable voters. Second, when campaigns contact voters long before election day and measure effects immediately—although this early persuasion decays. These findings contribute to ongoing debates about how political elites influence citizens’ judgments.
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.
It's Possible to Reduce Polarization without Reducing Engagement
The results of the Prosocial Ranking Challenge
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.
Undermining a Rival Party’s Issue Competence through Negative Campaigning: Experimental Evidence from the USA, Denmark, and Australia
Much party communication encourages voters to lower issue-related evaluations of rival parties. Yet, studies of such influence are rare. Drawing on research on political parties’ negative campaigning, this article starts to fill this gap. We triangulate evidence from four survey experiments across six issues in Denmark, the US, and Australia, and show that a party’s negative campaigning decreases voters’ evaluations of the target party’s issue-handling competence (i.e. issue ownership), but does not backlash on voters’ evaluations of the sponsor. Such attack on the target party does not have to be tied to a negative policy development like the crime rate to undermine the target party’s competence evaluations. At the same time, a negative policy development only undermines a party’s evaluations when it is accompanied by a rival party’s negative campaigning attack. The implications for party competition and the mass-elite linkage are important.
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.
It’s Not Polarization; It’s the Radicalization of the Political Right
Polarization has become the master concept for diagnosing contemporary democratic crises. The notion denotes three features: symmetry between parties, politics as an opinion space where positions diverge, and mutual repulsion between opposing camps. Yet none of these capture current realities. Across democracies, the central dynamic is not two poles drifting apart but the transformation of the political right into authoritarianism, norm breaking, and openness to political violence. Social democratic and center-right parties tend to respond in the opposite way from what “polarization” implies: by accommodating rightward. Attempts to salvage the polarization frame with modifiers (“asymmetric,” “affective,” “sectarian,” “pernicious”) concede these realities but risk hollowing out the concept’s definitional core. These limitations reveal a deeper misdiagnosis: when one party turns antidemocratic and illiberal, incivility and conflict are inevitable—but they are symptoms, not the root problem. Misdiagnosing them as the central issue leads to viewing civility and compromise as remedies, thereby risking the legitimation of authoritarian actors. This article proposes an alternative lens: the radicalization of the political right. Developed in the study of extremism, the radicalization framework better captures asymmetric change, identity-driven politics, and the mainstreaming of illiberalism. It foregrounds identity fusion, threat narratives, elite entrepreneurship, and escalation. Concepts are never politically innocent and persisting with “polarization” risks both misdiagnosing and normalizing authoritarian threats.
It’s Not Polarization; It’s the Radicalization of the Political Right
Polarization has become the master concept for diagnosing contemporary democratic crises. The notion denotes three features: symmetry between parties, politics as an opinion space where positions diverge, and mutual repulsion between opposing camps. Yet none of these capture current realities. Across democracies, the central dynamic is not two poles drifting apart but the transformation of the political right into authoritarianism, norm breaking, and openness to political violence. Social democratic and center-right parties tend to respond in the opposite way from what “polarization” implies: by accommodating rightward. Attempts to salvage the polarization frame with modifiers (“asymmetric,” “affective,” “sectarian,” “pernicious”) concede these realities but risk hollowing out the concept’s definitional core. These limitations reveal a deeper misdiagnosis: when one party turns antidemocratic and illiberal, incivility and conflict are inevitable—but they are symptoms, not the root problem. Misdiagnosing them as the central issue leads to viewing civility and compromise as remedies, thereby risking the legitimation of authoritarian actors. This article proposes an alternative lens: the radicalization of the political right. Developed in the study of extremism, the radicalization framework better captures asymmetric change, identity-driven politics, and the mainstreaming of illiberalism. It foregrounds identity fusion, threat narratives, elite entrepreneurship, and escalation. Concepts are never politically innocent and persisting with “polarization” risks both misdiagnosing and normalizing authoritarian threats.
Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor
Large language models (LLMs) are commonly evaluated for political bias based on their responses to fixed questionnaires, which typically place frontier models on the political left. A parallel literature shows that LLMs are sycophantic: they adapt their answers to the views, identities, and expectations of the user. We show that these findings are linked: standard political-bias audits partly capture sycophantic accommodation to the inferred auditor. We employ a factorial experiment across three major audit instruments--the Political Compass Test, the Pew Political Typology, and 1,540 partisan-benchmarked Pew American Trends Panel items--administered to six frontier LLMs while varying only the asker's stated identity (N = 30,990 responses). At baseline, all six models lean left. When the asker identifies as a conservative Republican, responses shift sharply: the share of items closer to Democrats falls by 28-62 percentage points, and all six models move right of center. A mirror-image progressive-Democrat cue produces little change; rightward accommodation is 8.0$\times$ larger than leftward. When asked who the default asker is, models identify an auditor, researcher, or academic; when asked what answer that asker expects, they select the Democrat-coded option 75% of the time, nearly the rate under an explicit progressive cue. These patterns are inconsistent with a purely fixed model ideology and indicate that single-prompt audits capture an interaction between model and inferred interlocutor. Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile that must be mapped across realistic interlocutors.
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 Elusive Backfire Effect: Mass Attitudes’ Steadfast Factual Adherence
Can citizens heed factual information, even when such information challenges their partisan and ideological attachments? The “backfire effect,” described by Nyhan and Reifler (Polit Behav 32(2):303–330. https://doi.org/10.1007/s11109-010-9112-2, 2010), says no: rather than simply ignoring factual information, presenting respondents with facts can compound their ignorance. In their study, conservatives presented with factual information about the absence of Weapons of Mass Destruction in Iraq became more convinced that such weapons had been found. The present paper presents results from five experiments in which we enrolled more than 10,100 subjects and tested 52 issues of potential backfire. Across all experiments, we found no corrections capable of triggering backfire, despite testing precisely the kinds of polarized issues where backfire should be expected. Evidence of factual backfire is far more tenuous than prior research suggests. By and large, citizens heed factual information, even when such information challenges their ideological commitments.
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 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.
Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility
As individuals and political leaders increasingly interact in online social networks, it is important to understand the dynamics of emotion perception online. Here, we propose that social media users overperceive levels of moral outrage felt by individuals and groups, inflating beliefs about intergroup hostility. Using a Twitter field survey, we measured authors’ moral outrage in real time and compared authors’ reports to observers’ judgements of the authors’ moral outrage. We find that observers systematically overperceive moral outrage in authors, inferring more intense moral outrage experiences from messages than the authors of those messages actually reported. This effect was stronger in participants who spent more time on social media to learn about politics. Preregistered confirmatory behavioural experiments found that overperception of individuals’ moral outrage causes overperception of collective moral outrage and inflates beliefs about hostile communication norms, group affective polarization and ideological extremity. Together, these results highlight how individual-level overperceptions of online moral outrage produce collective overperceptions that have the potential to warp our social knowledge of moral and political attitudes.
