Perceived risk, political polarization, and the willingness to follow COVID-19 mitigation guidelines

Abstract

Objective: Risk assessment and response is important for understanding human behavior. The divisive context surrounding the coronavirus pandemic inspires our exploration of risk perceptions and the polarization of mitigation practices (i.e., the degree to which the behaviors of people on the political "Left" diverge from those on the "Right"). Specifically, we investigate the extent to which the political polarization of willingness to comply with mitigation behaviors changes with risk perceptions.

Method: Analyses use data from two sources: an original dataset of Twitter posts and a nationally-representative survey. In the Twitter data, negative binomial regression models are used to predict mitigation intent measured using tweet counts. In the survey data, logit models predict self-reported mitigation behavior (vaccination, masking, and social distancing).

Results: Findings converged across both datasets, supporting the idea that the links between political orientation and willingness to follow mitigation guidelines depend on perceived risk. People on the Left are more inclined than their Right-oriented colleagues to follow guidelines, but this polarization tends to decrease as the perceived risk of COVID-19 intensifies. Additionally, we find evidence that exposure to COVID-19 infections sends ambiguous signals about the risk of the virus while COVID-19 related deaths have a more consistent impact on mitigation behaviors.

Conclusions: Pandemic-related risks can create opportunities for perceived "common ground," between the political "Right" and "Left." Risk perceptions and politics interact in their links to intended COVID-19 mitigation behavior (as measured both on Twitter and in a national survey). Our results invite a more complex interpretation of political polarization than those stemming from simplistic analyses of partisanship and ideology.

Keywords: Adherence; COVID-19; Disease mitigation behavior; Ideology; Political polarization; Public opinion; Risk perception; Social media.

Fig. 1

How the political polarization of adherence/compliance changes with risk perceptions (Hypotheses 1, 2, 3, and 4).

Fig. 2

The most significant words associated with the non-compliant and compliant labels as measured by Monroe et al.‘s Fightin’ Words statistic (Monroe et al., 2008).

Fig. 3

The distribution of ideology among users in our sample with local minimum and maxima indicated. Ideology is measured using tweetscores (Barberá, 2015), which places users on an uni-dimensional left-right scale based on the elites they follow.

Fig. 4

The ideological distribution of tweet authors by label. Ideology is measured using tweetscores (Barberá, 2015), which places users on an uni-dimensional left-right scale based on the elites they follow. The large spike in the non-compliant violin indicates the overwhelming majority of non-compliant tweets are authored by conservatives.

Fig. 5

The change in the estimated effect of ideology on non-compliant language as county level deaths increase. The negative slope indicates that the impact of ideology on non-compliant tweets is smaller in counties with higher death rates.

Fig. 6

Survey-weighted proportion of respondents adhering to mitigating behaviors by self-disclosed COVID-19 concern. Weights are constructed first using probability weighting with oversampling for select sub-populations, then by post-stratification to match American Community Survey demographics.

Fig. 7

Survey-weighted proportion of respondents adhering to mitigating behaviors by proximity to COVID-19 infection. Weights are constructed first using probability weighting with oversampling for select sub-populations, then by post-stratification to match American Community Survey demographics.

Fig. 8

Survey-weighted proportion of respondents adhering to mitigating behaviors by proximity to COVID-19 death. Weights are constructed first using probability weighting with oversampling for select sub-populations, then by post-stratification to match American Community Survey demographics.