Social media users are now regularly asking large language model (LLM)-powered bots to fact-check posts on social media. Here, we explore this phenomenon using an exhaustive sample of all k=1,671,841 instances from February through September 2025 in which users on X asked Grok or Perplexity to check the veracity of a post. We find that explicit requests for fact-checking account for about 5 percent of all messages sent to the LLM bots, with most of these requests focusing on politics, elections, and geopolitical events. Slightly more Republican-leaning users request fact-checks from Grok than Democratic-leaning users, while substantially more Democratic-leaning users request fact-checks from Perplexity than Republican-leaning users. Thus, we document the beginning of polarized attitudes towards specific AI models. Regardless of the requester's partisanship, however, fact-checks are significantly more likely to be requested on posts from Republican users - and Republican's posts are significantly more likely to be rated as false by both Grok and Perplexity. Next, we compare the results of fact-checks for the same post from different LLMs, and find high correlations across models. We also find a relatively high level of agreement between Grok, Perplexity, and Community Notes on posts that received both an LLM fact-check and a rated-helpful note; and a reasonably strong level of agreement between LLM ratings and the ratings of three professional fact-checkers on a sample of 100 claims. Together, these results highlight growing bi-partisan demand for LLM fact-checking integrated into social media platforms, and suggest that this approach - although far from perfect - has the potential to help identify and correct inaccurate content at scale.
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.
This paper investigates the growing role of emotions in shaping policy views. Analyzing social citizens’ media postings and political party messaging over a large variety of policy issues from 2013 to 2024, we document a sharp rise in negative emotions, particularly anger. Content generating anger drives significantly more engagement. We then conduct two nationwide online experiments in the U.S, exposing participants to video treatments that induce positive or negative emotions to measure their causal effects on policy views. The results show that negative emotions increase support for protectionism, restrictive immigration policies, redistribution, and climate policies but do not reinforce populist attitudes. In contrast, positive emotions have little effect on policy preferences but reduce populist inclinations. Finally, distinguishing between fear and anger, we find that anger exerts a much stronger influence on citizens’ policy views, in line with its growing presence in the political rhetoric.
We propose a new measure of conflict at a daily frequency using data from the Global Database of Events, Language, and Tone (GDELT). Our methodology allows us to disentangle conflict discussed by the media of a country from conflict involving the country in question and covered by global sources. We first demonstrate that the global index correlates with the Geopolitical Risk Index (GPR) from Caldara et al. (2020). Conflicts involving threats, force posture, and reductions in relations have the largest correlations with the GPR. We then analyze the relationship between conflict and stock returns across 44 countries over the period 2015-2023. We find that an increase in conflict is correlated with lower contemporaneous returns and that the primary drivers of market movements are assaults and reductions in relations. Both local coverage of global conflicts and global coverage of local conflicts affect stock returns, underscoring the importance of constructing more diverse indicators of geopolitical risk from a larger and more varied set of news sources.
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.