Leveraging AI for democratic discourse: Chat interventions can improve online political conversations at scale.

Political discourse is the soul of democracy, but misunderstanding and conflict can fester in divisive conversations. The widespread shift to online discourse exacerbates many of these problems and corrodes the capacity of diverse societies to cooperate in solving social problems. Scholars and civil society groups promote interventions that make conversations less divisive or more productive, but scaling these efforts to online discourse is challenging. We conduct a large-scale experiment that demonstrates how online conversations about divisive topics can be improved with AI tools. Specifically, we employ a large language model to make real-time, evidence-based recommendations intended to improve participants' perception of feeling understood. These interventions improve reported conversation quality, promote democratic reciprocity, and improve the tone, without systematically changing the content of the conversation or moving people's policy attitudes.

Perceived gender and political persuasion: a social media field experiment during the 2020 US Democratic presidential primary election.

Women have less influence than men in a variety of settings. Does this result from stereotypes that depict women as less capable, or biased interpretations of gender differences in behavior? We present a field experiment that-unbeknownst to the participants-randomized the gender of avatars assigned to Democrats using a social media platform we created to facilitate discussion about the 2020 Primary Election. We find that misrepresenting a man as a woman undermines his influence, but misrepresenting a woman as a man does not increase hers. We demonstrate that men's higher resistance to being influenced-and gendered word use patterns-both contribute to this outcome. These findings challenge prevailing wisdom that women simply need to behave more like men to overcome gender discrimination and suggest that narrowing the gap will require simultaneous attention to the behavior of people who identify as women and as men.

Reducing political polarization in the United States with a mobile chat platform.

Do anonymous online conversations between people with different political views exacerbate or mitigate partisan polarization? We created a mobile chat platform to study the impact of such discussions. Our study recruited Republicans and Democrats in the United States to complete a survey about their political views. We later randomized them into treatment conditions where they were offered financial incentives to use our platform to discuss a contentious policy issue with an opposing partisan. We found that people who engage in anonymous cross-party conversations about political topics exhibit substantial decreases in polarization compared with a placebo group that wrote an essay using the same conversation prompts. Moreover, these depolarizing effects were correlated with the civility of dialogue between study participants. Our findings demonstrate the potential for well-designed social media platforms to mitigate political polarization and underscore the need for a flexible platform for scientific research on social media.

An Online experiment during the 2020 US-Iran crisis shows that exposure to common enemies can increase political polarization.

A longstanding theory indicates that the threat of a common enemy can mitigate conflict between members of rival groups. We tested this hypothesis in a pre-registered experiment where 1670 Republicans and Democrats in the United States were asked to complete an online social learning task with a bot that was labeled as a member of the opposing party. Prior to this task, we exposed respondents to primes about (a) a common enemy (involving Iran and Russia); (b) a patriotic event; or (c) a neutral, apolitical prime. Though we observed no significant differences in the behavior of Democrats as a result of priming, we found that Republicans-and particularly those with very strong conservative views-were significantly less likely to learn from Democrats when primed about a common enemy. Because our study was in the field during the 2020 Iran Crisis, we were able to further evaluate this finding via a natural experiment-Republicans who participated in our study after the crisis were even less influenced by the beliefs of Democrats than those Republicans who participated before this event. These findings indicate common enemies may not reduce inter-group conflict in highly polarized societies, and contribute to a growing number of studies that find evidence of asymmetric political polarization in the United States. We conclude by discussing the implications of these findings for research in social psychology, political conflict, and the rapidly expanding field of computational social science.

Computational Social Science and Sociology.

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; (f) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017.

There is widespread concern that Russia and other countries have launched social-media campaigns designed to increase political divisions in the United States. Though a growing number of studies analyze the strategy of such campaigns, it is not yet known how these efforts shaped the political attitudes and behaviors of Americans. We study this question using longitudinal data that describe the attitudes and online behaviors of 1,239 Republican and Democratic Twitter users from late 2017 merged with nonpublic data about the Russian Internet Research Agency (IRA) from Twitter. Using Bayesian regression tree models, we find no evidence that interaction with IRA accounts substantially impacted 6 distinctive measures of political attitudes and behaviors over a 1-mo period. We also find that interaction with IRA accounts were most common among respondents with strong ideological homophily within their Twitter network, high interest in politics, and high frequency of Twitter usage. Together, these findings suggest that Russian trolls might have failed to sow discord because they mostly interacted with those who were already highly polarized. We conclude by discussing several important limitations of our study-especially our inability to determine whether IRA accounts influenced the 2016 presidential election-as well as its implications for future research on social media influence campaigns, political polarization, and computational social science.

Exposure to opposing views on social media can increase political polarization.

There is mounting concern that social media sites contribute to political polarization by creating "echo chambers" that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.

Using Internet search data to examine the relationship between anti-Muslim and pro-ISIS sentiment in U.S. counties.

Recent terrorist attacks by first- and second-generation immigrants in the United States and Europe indicate that radicalization may result from the failure of ethnic integration-or the rise of intergroup prejudice in communities where "home-grown" extremists are raised. Yet, these community-level drivers are notoriously difficult to study because public opinion surveys provide biased measures of both prejudice and radicalization. We examine the relationship between anti-Muslim and pro-ISIS (Islamic State of Iraq and Syria) Internet searches in 3099 U.S. counties between 2014 and 2016 using instrumental variable models that control for various community-level factors associated with radicalization. We find that anti-Muslim searches are strongly associated with pro-ISIS searches-particularly in communities with high levels of poverty and ethnic homogeneity. Although more research is needed to verify the causal nature of this relationship, this finding suggests that minority groups may be more susceptible to radicalization if they experience discrimination in settings where they are isolated and therefore highly visible-or in communities where they compete with majority groups for limited financial resources. We evaluate the validity of our findings using several other data sources and discuss the implications of our findings for the study of terrorism and intergroup relations, as well as immigration and counterterrorism policies.

Emotional Feedback and the Viral Spread of Social Media Messages About Autism Spectrum Disorders.

OBJECTIVE: To determine whether exchanges of emotional language between health advocacy organizations and social media users predict the spread of posts about autism spectrum disorders (ASDs). METHODS: I created a Facebook application that tracked views of ASD advocacy organizations' posts between July 19, 2011, and December 18, 2012. I evaluated the association between exchanges of emotional language and viral views of posts, controlling for additional characteristics of posts, the organizations that produced them, the social media users who viewed them, and the broader social environment. RESULTS: Exchanges of emotional language between advocacy organizations and social media users are strongly associated with viral views of posts. CONCLUSIONS: Social media outreach may be more successful if organizations invite emotional dialogue instead of simply conveying information about ASDs. Yet exchanges of angry language may contribute to the viral spread of misinformation, such as the rumor that vaccines cause ASDs.

Cultural carrying capacity: Organ donation advocacy, discursive framing, and social media engagement.

Social media sites such as Facebook have become a powerful tool for public health outreach because they enable advocacy organizations to influence the rapidly increasing number of people who frequent these forums. Yet the very open-ness of social media sites creates fierce competition for public attention. The vast majority of social media messages provoke little or no reaction because of the sheer volume of information that confronts the typical social media user each day. In this article, I present a theory of the "cultural carrying capacity" of social media messaging campaigns. I argue that advocacy organizations inspire more endorsements, comments, and shares by social media users if they diversify the discursive content of their messages. Yet too much diversification creates large, disconnected audiences that lack the sense of shared purpose necessary to sustain an online movement. To evaluate this theory, I created a Facebook application that collects social media posts produced by forty-two organ donation advocacy organizations over 1.5 years, as well as supplemental information about the organization, its audience, and the broader social context in which they interact. Time series models provide strong evidence for my theory net of demographic characteristics of social media users, the resources and tactics of each organization, and broader external factors. I conclude by discussing the implications of these findings for public health, cultural sociology, and the nascent field of computational social science.