Affective polarization, local contexts and public opinion in America

Druckman, James N.; Klar, Samara; Krupnikov, Yanna; Levendusky, Matthew; Ryan, Joseph B.

doi:10.1038/s41562-020-01012-5

Cited by 272 publications

(245 citation statements)

References 62 publications

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“…This is followed by a pointed discussion concerning the politicized nature of COVID-19, where we draw on work on political misperceptions. This speaks to the reality that in the United States, COVID-19 quickly became politicized (e.g., Allcott et al, 2020;Gollwitzer et al, 2020;Druckman et al, 2021).…”

Section: Misinformation and Misperceptionsmentioning

confidence: 99%

“…President Trump -with his dismissal of the virus, demands to reopen the economy, and refusal to wear a mask -is the apotheosis of this trend, but is far from the only example of it, as Democratic governors typically took swifter and more public actions to combat the virus than did most Republican governors (Fowler et al, 2020). These elite cues affected partisans' behaviors with Democrats engaging in more precautionary measures and Republicans doing the reverse -a trend, for both parties, that was particularly notable for strong partisans (e.g., those with high levels of affective polarization) (Druckman et al, 2021).…”

Section: Misinformation and Misperceptionsmentioning

confidence: 99%

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The role of race, religion, and partisanship in misperceptions about COVID-19

Druckman

Ognyanova

Baum³

et al. 2021

Group Processes & Intergroup Relations

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Concerns about misperceptions among the public are rampant. Yet, little work explores the correlates of misperceptions in varying contexts – that is, how do factors such as group affiliations, media exposure, and lived experiences correlate with the number of misperceptions people hold? We address these questions by investigating misperceptions about COVID-19, focusing on the role of racial/ethnic, religious, and partisan groups. Using a large survey, we find the number of correct beliefs held by individuals far dwarfs the number of misperceptions. When it comes to misperceptions, we find that minorities, those with high levels of religiosity, and those with strong partisan identities – across parties – hold a substantially greater number of misperceptions than those with contrasting group affiliations. Moreover, we show other variables (e.g., social media usage, number of COVID-19 cases in one’s county) do not have such strong relationships with misperceptions, and the group-level results do not reflect acquiescence to believing any information regardless of its truth value. Our results accentuate the importance of studying group-level misperceptions on other scientific and political issues and developing targeted interventions for these groups.

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Section: Misinformation and Misperceptionsmentioning

confidence: 99%

Section: Misinformation and Misperceptionsmentioning

confidence: 99%

The role of race, religion, and partisanship in misperceptions about COVID-19

Druckman

Ognyanova

Baum³

et al. 2021

Group Processes & Intergroup Relations

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although other surveybased analyses have studied individual health behavior in response to COVID-19, ours is unique in two ways: it captures early evidence across the widest range of behaviors and attitudes available at the onset of the pandemic. This is in contrast to work that focuses on partisan affect at a later point in the pandemic [31] or that focuses more narrowly on self-reported social distancing only [32]. Relative to existing individual-level survey research on partisanship and health behavior in the early stages of the US pandemic [28,33], our analysis covers a much wider array of policy responses and attitudes.…”

Section: Introductionmentioning

confidence: 99%

Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic

2021

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Objective To study the U.S. public’s health behaviors, attitudes, and policy opinions about COVID-19 in the earliest weeks of the national health crisis (March 20–23, 2020). Method We designed and fielded an original representative survey of 3,000 American adults between March 20–23, 2020 to collect data on a battery of 38 health-related behaviors, government policy preferences on COVID-19 response and worries about the pandemic. We test for partisan differences COVID-19 related policy attitudes and behaviors, measured in three different ways: party affiliation, intended 2020 Presidential vote, and self-placed ideological positioning. Our multivariate approach adjusts for a wide range of individual demographic and geographic characteristics that might confound the relationship between partisanship and health behaviors, attitudes, and preferences. Results We find that partisanship—measured as party identification, support for President Trump, or left-right ideological positioning—explains differences in Americans across a wide range of health behaviors and policy preferences. We find no consistent evidence that controlling for individual news consumption, the local policy environment, and local pandemic-related deaths erases the observed partisan differences in health behaviors, beliefs, and attitudes. In further analyses, we use a LASSO regression approach to select predictors, and find that a partisanship indicator is the most commonly selected predictor across the 38 dependent variables that we study. Conclusion Our analysis of individual self-reported behavior, attitudes, and policy preferences in response to COVID-19 reveals that partisanship played a central role in shaping individual responses in the earliest months of the COVID-19 pandemic. These results indicate that partisan differences in responding to a national public health emergency were entrenched from the earliest days of the pandemic.

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“…According to recently released American National Election Studies data, affective polarization grew particularly steeply from 2016 to 2020, reaching its highest point in 40 y. Outparty animosity, more so than in-party warmth, has also become a more powerful predictor of important behaviors, such as voting behavior (46) and the sharing of political fake news (39). When out-party animosity is strong, partisans are motivated to distinguish themselves from the out-party (by, for instance, holding opinions that are distinct from the out-party) (47). While some research suggests that out-group cues might be more powerful than in-group cues (48), there is still debate about the extent to which partisan belief and behavior is driven by in-group favoritism versus out-group derogation (49).…”

mentioning

confidence: 99%

Out-group animosity drives engagement on social media

Rathje

Bavel

Linden

2021

Proc. Natl. Acad. Sci. U.S.A.

247

188

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There has been growing concern about the role social media plays in political polarization. We investigated whether out-group 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 out-group were shared or retweeted about twice as often as posts about the in-group. Each individual term referring to the political out-group increased the odds of a social media post being shared by 67%. Out-group language consistently emerged as the strongest predictor of shares and retweets: the average effect size of out-group 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 out-group was a very strong predictor of “angry” reactions (the most popular reactions across all datasets), and language about the in-group was a strong predictor of “love” reactions, reflecting in-group favoritism and out-group derogation. This out-group 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, out-group 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 out-group animosity.

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Affective polarization, local contexts and public opinion in America

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Cited by 272 publications

References 62 publications

The role of race, religion, and partisanship in misperceptions about COVID-19

The role of race, religion, and partisanship in misperceptions about COVID-19

Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic

Out-group animosity drives engagement on social media

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