Right-wing populist parties have become a major challenge to liberal democracies worldwide. How can mainstream parties effectively counter their appeal? While prior research has focused on the drivers of populist support, much less is known about how to counter right-wing populist rhetoric. We test four communication counter-strategies that target core elements of right-wing populist appeals: (i) undermining populists’ claims to democratic credibility, (ii) promoting inclusive in-group identity to counter exclusionary narratives, (iii) highlighting their performance legitimacy, and (iv) emphasizing the procedural legitimacy of mainstream parties. Drawing on real-world party communications from social media, manifestos, and press releases, we created 170 experimental treatments (posters and social media posts varying in format and complexity) to simulate how voters encounter political messages in everyday life. In a large-scale survey experiment with over 24,000 respondents in Germany, messages undermining right-wing populists’ democratic credibility prove most effective, particularly among citizens with high initial support for such parties.
Media Cues and Citizen Support for Right-Wing Populist Parties
Abstract. Right-wing populist parties are thriving across Europe. Their success is usually attributed to demand-side voter factors and supply-side factors
Mainstreaming the populist radical right? Online news exposure and voting behavior in the 2019 European Parliament election
How does media exposure relate to support for radical right populist parties (RRPPs)? We contribute to this classic debate by analyzing the web browsing histories and survey responses of six thousand study participants in France, Germany, Italy, Spain, and the UK during the 2019 European Parliament election. Linking direct measures of online news exposure to voting behavior allows us to assess the effects of the salience of issues politicized by RRPPs on their electoral support. The likelihood to vote for RRPPs was higher when the EU was more salient in individual media diets, while exposure to the less salient issue of immigration did not increase the propensity to vote for RRPPs. Alongside consistent results for other party families and interactions with pre-existing voting intentions, the findings indicate that the electoral effects of online media are contingent on the overall salience of a specific issue and voters’ predispositions.
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.
The Populist Dynamic: Experimental Evidence on the Effects of Countering Populism
We evaluate how traditional parties may respond to populist parties on issues aligning with populist messages. During the 2020 Italian referendum on the reduction of members of Parliament, we conducted a large-scale field experiment, exposing 200 municipalities to nearly a million impressions of programmatic advertisement. Our treatments comprised two video ads against the reform: one debunking populist rhetoric and another attributing blame to populist politicians. This anti-populist campaign proved effective through demobilization, as it reduced both turnout and the votes in favor of the reform. Notably, the effects were more pronounced in municipalities with lower rates of college graduates, higher unemployment, and a history of populist votes. This exogenous influence introduced a unique populist dynamic, observable in the 2022 national election where treated municipalities showed increased support for Brothers of Italy, a rising populist party, and decreased support for both traditional parties and the populists behind the 2020 reform. A follow-up survey further showed increased political interest and diminished trust in political institutions among the residents of municipalities targeted by the campaign.
Persuasion and resistance: A survey experiment on messaging for and against One Nation | School of Politics & International Relations
Since the May 2025 federal election, One Nation's primary vote has risen sharply across published polling. If an election were held today, it would likely be the second-largest bloc in the House of Representatives. What drives this surge — and what, if anything, suppresses it — remains contested, with explanations ranging from economic grievance and cultural backlash to anti-establishment disaffection. While these accounts have been tested experimentally for radical-right parties abroad, they remain largely untested in Australia.
When Do Parties Lie? Misinformation and Radical-Right Populism Across 26 Countries - Petter Törnberg, Juliana Chueri, 2026
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 mi...
Improving social media for democratic discourse
Social media have expanded opportunities for communication and political participation, but today's dominant platforms are optimized primarily for engagement and advertising revenue, contributing...
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.
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.
Facts and Myths about Misperceptions
Misperceptions threaten to warp mass opinion and public policy on controversial issues in politics, science, and health. What explains the prevalence and persistence of these false and unsupported beliefs, which seem to be genuinely held by many people? Though limits on cognitive resources and attention play an important role, many of the most destructive misperceptions arise in domains where individuals have weak incentives to hold accurate beliefs and strong directional motivations to endorse beliefs that are consistent with a group identity such as partisanship. These tendencies are often exploited by elites who frequently create and amplify misperceptions to influence elections and public policy. Though evidence is lacking for claims of a “post-truth” era, changes in the speed with which false information travels and the extent to which it can find receptive audiences require new approaches to counter misinformation. Reducing the propagation and influence of false claims will require further efforts to inoculate people in advance of exposure (for example, media literacy), debunk false claims that are already salient or widespread (for example, fact-checking), reduce the prevalence of low-quality information (for example, changing social media algorithms), and discourage elites from promoting false information (for example, strengthening reputational sanctions).
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.
GermanPartiesQA: Benchmarking Commercial Large Language Models and AI Companions for Political Alignment and Sycophancy
Large language models (LLMs) are increasingly shaping citizens’ information ecosystems. Products incorporating LLMs, such as chatbots and AI Companions, are now widely used for decision support and information retrieval, including in sensitive domains, raising concerns about hidden biases and growing potential to shape individual decisions and public opinion. This paper introduces GermanPartiesQA, a benchmark of 418 political statements from German Voting Advice Applications across 11 elections to evaluate six commercial LLMs. We evaluate their political alignment based on role-playing experiments with political personas. Our evaluation reveals three specific findings: (1) Factual limitations: LLMs show limited ability to accurately generate factual party positions, particularly for centrist parties. (2) Model-specific ideological alignment: We identify consistent alignment patterns and degree of political steerability for each model across temperature settings and experiments. (3) Claim of sycophancy: While models adjust to political personas during role-play, we find this reflects persona-based steerability rather than the increasingly popular, yet contested concept of sycophancy. Our study contributes to evaluating the political alignment of closed-source LLMs that are increasingly embedded in electoral decision support tools and AI Companion chatbots.
GermanPartiesQA: Benchmarking Commercial Large Language Models and AI Companions for Political Alignment and Sycophancy
Large language models (LLMs) are increasingly shaping citizens’ information ecosystems. Products incorporating LLMs, such as chatbots and AI Companions, are now widely used for decision support and information retrieval, including in sensitive domains, raising concerns about hidden biases and growing potential to shape individual decisions and public opinion. This paper introduces GermanPartiesQA, a benchmark of 418 political statements from German Voting Advice Applications across 11 elections to evaluate six commercial LLMs. We evaluate their political alignment based on role-playing experiments with political personas. Our evaluation reveals three specific findings: (1) Factual limitations: LLMs show limited ability to accurately generate factual party positions, particularly for centrist parties. (2) Model-specific ideological alignment: We identify consistent alignment patterns and degree of political steerability for each model across temperature settings and experiments. (3) Claim of sycophancy: While models adjust to political personas during role-play, we find this reflects persona-based steerability rather than the increasingly popular, yet contested concept of sycophancy. Our study contributes to evaluating the political alignment of closed-source LLMs that are increasingly embedded in electoral decision support tools and AI Companion chatbots.
Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation
Social media platforms have been widely linked to societal harms, including rising polarization and the erosion of constructive debate. Can these problems be mitigated through prosocial interventions? We address this question using a novel method - generative social simulation - that embeds Large Language Models within Agent-Based Models to create socially rich synthetic platforms. We create a minimal platform where agents can post, repost, and follow others. We find that the resulting following-networks reproduce three well-documented dysfunctions: (1) partisan echo chambers; (2) concentrated influence among a small elite; and (3) the amplification of polarized voices - creating a 'social media prism' that distorts political discourse. We test six proposed interventions, from chronological feeds to bridging algorithms, finding only modest improvements - and in some cases, worsened outcomes. These results suggest that core dysfunctions may be rooted in the feedback between reactive engagement and network growth, raising the possibility that meaningful reform will require rethinking the foundational dynamics of platform architecture.
Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation
Social media platforms have been widely linked to societal harms, including rising polarization and the erosion of constructive debate. Can these problems be mitigated through prosocial interventions? We address this question using a novel method - generative social simulation - that embeds Large Language Models within Agent-Based Models to create socially rich synthetic platforms. We create a minimal platform where agents can post, repost, and follow others. We find that the resulting following-networks reproduce three well-documented dysfunctions: (1) partisan echo chambers; (2) concentrated influence among a small elite; and (3) the amplification of polarized voices - creating a 'social media prism' that distorts political discourse. We test six proposed interventions, from chronological feeds to bridging algorithms, finding only modest improvements - and in some cases, worsened outcomes. These results suggest that core dysfunctions may be rooted in the feedback between reactive engagement and network growth, raising the possibility that meaningful reform will require rethinking the foundational dynamics of platform architecture.