In this paper, we document the rise of anger in U.S. political communication and study its effects on policy views. We combine online and offline data, including the full record of congressional floor speeches since the 1870s, tweets from members of Congress, tweets from voters matched to electoral registries, campaign speeches, and Reddit comments. Anger in U.S. congressional speeches has risen sharply in the last decade, reaching its highest level in a century and a half. In voter tweets, the share of policy-related sentences expressing anger rises 35% between 2013 and 2025. In tweets by members of Congress, the angry-sentence share rises by 72%. Angry tweets by members of Congress or by voters receive around 60% more retweets relative to tweets with no emotion. Our two experiments show that negative emotions increase support for protectionism, restrictive immigration policies, and redistribution. Positive emotions mainly improve perceptions and reduce support for populism. Anger also changes climate policy views, while fear has no detectable effect. Building on the ACTORC framework of Bordalo et al. ( 2026 ), our model shows how emotions direct attention toward different features of policy problems. Overall, emotions shape both engagement and policy views.
Large language models (LLMs) can both correct inaccurate beliefs and manipulate attitudes. As LLMs are increasingly deployed as fact-checkers on social media, their impact depends not only on the models' technical capability, but also on the choices of their designers and the attitudes and behaviors of platform users. Here, we shed light on all three dimensions of this sociotechnical system. We analyze an exhaustive sample of 1,555,275 English-language fact-check requests sent to Grok and Perplexity bots on X from February through September 2025 and a random sample of 184,123 fact-checks from Grok from September 2025 to August 2026. We also benchmark 19 frontier LLMs against professional fact-checkers' evaluations of hundreds of news headlines, and conduct a preregistered survey experiment (N=1,605) to evaluate correction efficacy. On the user side, Grok requesters skew heavily Republican; familiarity with and trust in specific LLMs vary substantially, such that trust in AI is no longer a unitary construct; and revealing that a fact-check comes from Grok reduces belief updating among Democrats. Examining LLM outputs, we find that from a technical perspective, LLM fact-checks can match or exceed agreement among professional fact-checkers. Yet we also show that this capacity is readily undermined by designers' choices: for example, restricting sources (e.g. Truth Social AI) or disabling search can degrade accuracy. Designers and users, more so than technical capability, are now the primary binding constraints.
How does the success of the far right affect the immigration rhetoric of mainstream parties and their electoral performance? To explore this question, we exploit the gradual success of the Front National in French parliamentary elections since its creation in 1972. We find that the electoral success of far-right parties came at the expense of a decline in the vote share of mainstream right-wing parties. Using a comprehensive textual analysis of the universe of candidate manifestos from 1968 to 1997, we show that mainstream right-wing candidates respond to the success of far-right parties by increasing the salience of immigration in their manifestos. This strategic adjustment mitigates electoral losses to far-right competitors. In contrast, the strategic response of mainstream left-wing candidates is to reduce the prominence of immigration in their manifestos to reduce their electoral losses.
Radio music quotas aim to boost exposure to domestic repertoire, but do they shape listening preferences beyond the broadcast context? We investigate whether quota-induced exposure translates into persistent preferences observable on streaming platforms. To do so, we combine an original cross-country database of quota policies with weekly Spotify Top 200 charts spanning 71 countries and individual-level listening histories from Deezer (one of France's leading streaming platforms) matched to survey data on radio exposure. At the aggregate level, quota countries exhibit lower domestic consumption shares on Spotify; however, this pattern likely reflects the endogenous adoption of such policies in markets where domestic content was initially underrepresented. Exploiting the 2024 Greek quota reform as a quasi-experiment, we find no evidence of an increase in national-language streaming consumption, using a synthetic difference-in-differences design. At the individual level, radio listening in France, where a radio quota applies, shows no association with stronger preferences for domestic music once demographic characteristics are accounted for. Overall, our findings provide no evidence that radio music quotas generate meaningful spillover effects on domestic music consumption in the streaming era.
Concerns about inauthentic accounts, including foreign actors posing as domestic voices, are central to debates about online discourse. Yet, little is known about accounts with inaccurate location claims and how they behave when discrepancies between their claimed and actual locations become publicly visible. In November 2025, X introduced an "About this account" feature that discloses each account's platform-inferred location of operation. We leverage this intervention in a large-scale quasi-experimental study of 8,200 politically engaged accounts claiming a U.S. location, comparing accounts whose disclosed locations matched versus contradicted their claims across 1.3 million posts and 3.6 million replies over 21 weeks. Before disclosure, location-mismatched accounts posted more misleading, scam-related, and cryptocurrency-related content, but showed no distinctive partisan leaning. Difference-in-differences estimates show that disclosure reduced the posting activity of location-mismatched accounts by 13.1% with the largest declines among accounts revealed to be in Africa (29.2%) and Asia (24.4%), and among accounts with VPN flags, username changes, or scam- and crypto-heavy content. Additionally, the decline in their replies was concentrated in interactions with U.S.-based recipients (10.3%), whereas replies to non-U.S.-based recipients showed no statistically significant change. Conversely, there was no significant change in average audience engagement with their posts. Location transparency thus works primarily by inducing restraint among the disclosed accounts rather than by shifting audience behaviour, and the accounts it constrains look at least as much like cross-border fraud as foreign political influence.
with Y. Chuai, N. Pröllochs, G. Lenzini, and Mohsen Mosleh
Community-based fact-checking can reduce the spread of annotated misleading posts, but whether it produces lasting behavioral change among corrected authors remains unclear. Here, we conduct a large-scale quasi-experimental study of the Community Notes system on X (formerly Twitter), tracking four weeks of activity before and after note display for 19,854 accounts and 57,935 corrections (noted posts and matched controls), covering 11,909,591 original posts. Difference-in-Differences estimates show that note display is followed by an average 2.9% increase in corrected accounts' original-post activity. This aggregate conceals two divergent trajectories. Accounts corrected only once reduce their activity by 2.4% and subsequently publish less toxic and less misleading content. Repeatedly corrected accounts, which constitute 28.6% of corrected accounts but produce 73.4% of fact-checked posts, instead increase their activity by 4.4% after their first correction and show no detectable response to later ones. They exhibit no comparable content improvement, and instead publish more highly misleading posts, cite lower-quality domains, and post more political content. Community notes can thus constrain individual misleading posts without durably improving the behavior of the accounts most responsible for them, indicating that correcting content and changing its producers are distinct objectives for platform design.
We propose a novel measure of investor attention by analyzing messages sent on Twitter around European Central Bank announcements. We then examine the market impact of the ECB decisions and press conferences, contingent on the level of investor attention prior to the announcements, across a wide array of assets (stocks, bonds, OIS, and exchange rates). We find that absolute price changes are higher when investor attention is elevated before the announcements. This effect is stronger in the press release window than in the press conference window, especially when focusing on messages from users who self-describe themselves as investors. Investor attention also magnifies the impact of monetary policy surprises. Our results suggest that central bankers can use the level of attention prior to the announcements to enhance their anticipation of the market's reaction to the announcement.