Numerous polls suggest that COVID-19 is a profoundly partisan issue in the United States. Using the geotracking data of 15 million smartphones per day, we found that US counties that voted for Donald Trump (Republican) over Hillary Clinton (Democrat) in the 2016 presidential election exhibited 14% less physical distancing between March and May 2020. Partisanship was more strongly associated with physical distancing than numerous other factors, including counties' COVID-19 cases, population density, median income, and racial and age demographics. Contrary to our predictions, the observed partisan gap strengthened over time and remained when stay-at-home orders were active. Additionally, county-level consumption of conservative media (Fox News) was related to reduced physical distancing. Finally, the observed partisan differences in distancing were associated with subsequently higher COVID-19 infection and fatality growth rates in pro-Trump counties. Taken together, these data suggest that US citizens' responses to COVID-19 are subject to a deep-and consequential-partisan divide.

What is the role of emotion in susceptibility to believing fake news? Prior work on the psychology of misinformation has focused primarily on the extent to which reason and deliberation hinder versus help the formation of accurate beliefs. Several studies have suggested that people who engage in more reasoning are less likely to fall for fake news. However, the role of reliance on emotion in belief in fake news remains unclear. To shed light on this issue, we explored the relationship between experiencing specific emotions and believing fake news (Study 1; N = 409). We found that across a wide range of specific emotions, heightened emotionality at the outset of the study was predictive of greater belief in fake (but not real) news posts. Then, in Study 2, we measured and manipulated reliance on emotion versus reason across four experiments (total N = 3884). We found both correlational and causal evidence that reliance on emotion increases belief in fake news: self-reported use of emotion was positively associated with belief in fake (but not real) news, and inducing reliance on emotion resulted in greater belief in fake (but not real) news stories compared to a control or to inducing reliance on reason. These results shed light on the unique role that emotional processing may play in susceptibility to fake news.

What is the role of emotion in susceptibility to believing fake news? Prior work on the psychology of misinformation has focused primarily on the extent to which reason and deliberation hinder versus help the formation of accurate beliefs. Several studies have suggested that people who engage in more reasoning are less likely to fall for fake news. However, the role of reliance on emotion in belief in fake news remains unclear. To shed light on this issue, we explored the relationship between specific emotions and belief in fake news (Study 1; N = 409). We found that across a wide range of specific emotions, heightened emotionality was predictive of increased belief in fake (but not real) news. Then, in Study 2, we measured and manipulated reliance on emotion versus reason across four experiments (total N = 3884). We found both correlational and causal evidence that reliance on emotion increases belief in fake news: Self-reported use of emotion was positively associated with belief in fake (but not real) news, and inducing reliance on emotion resulted in greater belief in fake (but not real) news stories compared to a control or to inducing reliance on reason. These results shed light on the unique role that emotional processing may play in susceptibility to fake news.

Social distancing is currently the single most effective method to reduce the spread of COVID-19. As such, researchers across varying fields are rushing to identify variables that predict social distancing and which interventions can heighten social distancing. Yet, much of this research relies on self-report measures (in part because of social distancing guidelines themselves). In two studies we examine whether self-reported social distancing overlaps with real-world behavior. In Study 1, individuals’ self-reported social distancing predicted decreased movement as quantified by participants’ average daily step-counts (assessed via smartphone pedometers). For every increase of one in self-reported social distancing (z-scored), individuals’ daily steps decreased by approximately 21% (Exp(B) ~ .79). In Study 2, the degree of self-reported social distancing in different U.S. States predicted the degree to which people in those States reduced their overall movement and travel to non-essential retail as assessed by ~17 million smart-phone GPS coordinates (.34 < rs < .57). Collectively, our results indicate that self-report measures of social distancing track actual behavior both at the individual and at the group level.

Few things bind disparate groups together like a common challenge. Yet, numerous polls suggest that the current COVID-19 pandemic in the U.S. is subject to a partisan divide. Using the geotracking data of 15 million smartphones per day, we show that counties that voted for Donald Trump over Hillary Clinton in 2016 exhibited 14% less physical distancing between March and May, 2020. Partisanship was a stronger predictor of physical distancing than numerous other factors, including counties’ median income, COVID-19 cases, and racial and age make-up. Contrary to our predictions, this finding strengthened over time and remained when stay-at-home orders were active. Additionally, counties’ consumption of conservative media (Fox News) predicted reduced physical distancing. Finally, reduced physical distancing in pro-Trump counties was associated with subsequently higher COVID-19 infection and fatality growth rates. Taken together, these data suggest that U.S. responses towards COVID-19 are subject to a deep partisan divide.

Americans are much more likely to be socially connected to copartisans, both in daily life and on social media. However, this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot’s strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from copartisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting and have important implications for political psychology, social media, and the politically polarized state of the American public.

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In an effort to combat COVID-19 and future pandemics, researchers have attempted to identify the factors underlying social distancing. Yet, much of this research relies on self-report measures. In two studies, we examine whether self-reported social distancing predicts objective distancing behavior. In Study 1, individuals’ self-reported social distancing predicted decreased mobility (assessed via smartphone step counts) during the COVID-19 pandemic. While participants high in self-reported distancing (+1 SD) exhibited a 33% reduction in daily step counts, those low in distancing (−1 SD) exhibited only a 3% reduction. Study 2 extended these findings to the group level. Self-reported social distancing at the U.S. state level accounted for 20% of the variance in states’ objective reduction in overall movement and visiting nonessential services (calculated via the GPS coordinates of ∼15 million people). Collectively, our results indicate that self-reported social distancing tracks actual social distancing behavior.