There is considerable debate over whether and how social media contributes to polarization. In a correlational study (n1 = 1,447) and two digital field experiments (n2 = 494, n3 = 1,133), we examined whether (un)following hyperpartisan social media influencers contributes to polarization and misinformation sharing. We found that incentivizing Twitter/X users to unfollow hyperpartisan social media influencers improved feelings toward the out-party by 23.5% compared to the control group, with effects persisting for at least six months. Unfollowing also led participants to engage with more accurate news and increased satisfaction with their Twitter/X feeds—without reducing engagement. This study demonstrates the long-term causal impact of exposure to hyperpartisan influencers. Moreover, unlike other social media reduction interventions, unfollowing is a targeted approach: like a scalpel, it surgically removes a few harmful parts of one’s feed, allowing the beneficial aspects to remain.

The social and behavioral sciences have been increasingly using automated text analysis to measure psychological constructs in text. We explore whether GPT, the large-language model underlying the artificial intelligence chatbot ChatGPT, can be used as a tool for automated psychological text analysis in several languages. Across 15 datasets (n = 47,925 manually annotated tweets and news headlines), we tested whether different versions of GPT (3.5 Turbo, 4, and 4 Turbo) can accurately detect psychological constructs (sentiment, discrete emotions, offensiveness, and moral foundations) across 12 languages. We found that GPT (r = 0.59-0.77) performs much better than English-language dictionary analysis (r = 0.20-0.30) at detecting psychological constructs as judged by manual annotators. GPT performs nearly as well as, and sometimes better than, several top-performing fine-tuned machine learning models. Moreover, GPT’s performance has improved across successive versions of the model, particularly for lesser-spoken languages. Overall, GPT may be superior to many existing methods of automated text analysis, since it achieves relatively high accuracy across many languages, requires no training data, and is easy to use with simple prompts (e.g., “is this text negative?”) and little coding experience. We provide sample code and a video tutorial for analyzing text with the GPT application programming interface. We argue that GPT and other large-language models may democratize automated text analysis by making advanced natural language processing capabilities more accessible, and may help facilitate more cross-linguistic research with understudied languages.

Recent studies have documented the type of content that is most likely to spread widely, or go “viral” on social media, yet little is known about people’s perceptions of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a sample of participants that is nationally representative of the US population (according to age, gender, and race/ethnicity) and surveyed them about their perceptions of social media virality (n = 511). In line with prior research, people believe that divisive content, moral outrage, negative content, high-arousal content, and misinformation are all likely to go viral online. However, they reported that this type of content should not go viral on social media. Instead, people reported that many forms of positive content – such as accurate content, nuanced content, and educational content – are not likely to go viral, even though they think this content should go viral. Importantly, these perceptions were shared among most participants, and were only weakly related to political orientation, social media usage, and demographic variables. In sum, there is broad consensus around the type of content people think social media platforms should and should not amplify, which can help inform solutions for improving social media.

The extent to which belief in (mis)information reflects lack of knowledge versus a lack of motivation to be accurate is unclear. Here, across four experiments (n = 3,364), we motivated US participants to be accurate by providing financial incentives for correct responses about the veracity of true and false political news headlines. Financial incentives improved accuracy and reduced partisan bias in judgements of headlines by about 30%, primarily by increasing the perceived accuracy of true news from the opposing party (d = 0.47). Incentivizing people to identify news that would be liked by their political allies, however, decreased accuracy. Replicating prior work, conservatives were less accurate at discerning true from false headlines than liberals, yet incentives closed the gap in accuracy between conservatives and liberals by 52%. A non-financial accuracy motivation intervention was also effective, suggesting that motivation-based interventions are scalable. Altogether, these results suggest that a substantial portion of people’s judgements of the accuracy of news reflects motivational factors.

Understanding how vaccine hesitancy relates to online behavior is crucial for addressing current and future disease outbreaks. We combined survey data measuring attitudes toward the COVID-19 vaccine with Twitter data in two studies (N1 = 464 Twitter users, N2 = 1,600 Twitter users) with preregistered hypotheses to examine how real-world social media behavior is associated with vaccine hesitancy in the United States (US) and the United Kingdom (UK). In Study 1, we found that following the accounts of US Republican politicians or hyper-partisan/low-quality news sites were associated with lower confidence in the COVID-19 vaccine—even when controlling for key demographics such as self-reported political ideology and education. US right-wing influencers (e.g. Candace Owens, Tucker Carlson) had followers with the lowest confidence in the vaccine. Network analysis revealed that participants who were low and high in vaccine confidence separated into two distinct communities (or “echo chambers”), and centrality in the more right-wing community was associated with vaccine hesitancy in the US, but not in the UK. In Study 2, we found that one’s likelihood of not getting the vaccine was associated with retweeting and favoriting low-quality news websites on Twitter. Altogether, we show that vaccine hesitancy is associated with following, sharing, and interacting with low-quality information online, as well as centrality within a conservative-leaning online community in the US. These results illustrate the potential challenges of encouraging vaccine uptake in a polarized social media environment.

There has been growing concern about the role social media plays in political polarization. We investigated whether outgroup animosity was particularly successful at generating engagement on two of the largest social media platforms: Facebook and Twitter. Analyzing posts from news media accounts and US congressional members (n = 2,730,215), we found that posts about the political outgroup were shared or retweeted about twice as often as posts about the ingroup. Each individual term referring to the political outgroup increased the odds of a social media post being shared by 67%. Outgroup language consistently emerged as the strongest predictor of shares and retweets: the average effect size of outgroup language was about 4.8 times as strong as that of negative affect language, and about 6.7 times as strong as that of moral-emotional language – both established predictors of social media engagement. Language about the outgroup was a very strong predictor of “angry” reactions (the most popular reactions across all datasets), and language about the ingroup was a strong predictor of “love” reactions, reflecting ingroup favoritism and outgroup derogation. This outgroup effect was not moderated by political orientation or social media platform, but stronger effects were found among political leaders than among news media accounts. In sum, outgroup language is the strongest predictor of social media engagement across all relevant predictors measured, suggesting that social media may be creating perverse incentives for content expressing outgroup animosity.

Can attending live theatre improve empathy by immersing audience members in the stories of others? We tested this question across three field studies (n = 1622), including a pre-registered replication. We randomly assigned audience members to complete surveys either before or after seeing plays, and measured the effects of the plays on empathy, attitudes, and pro-social behavior. After, as compared to before, seeing the plays, people reported greater empathy for groups depicted in the shows, held opinions that were more consistent with socio-political issues highlighted in the shows, and donated more money to charities related to the shows. Seeing theatre also led participants to donate more to charities unrelated to the shows, suggesting that theatre’s effects on pro-sociality generalize to different contexts. Altogether, these findings suggest that theatre is more than mere entertainment; it can lead to tangible increases in empathy and pro-social behavior.