Politically Contested Beliefs: Support for Trump Better Predicts Having Inaccurate Beliefs About COVID-19 Than Conservative/Republican Political Identity

ABSTRACT

The COVID-19 pandemic poses an unprecedented risk to society. Studies suggested that people’s beliefs about COVID-19 are divided depending on partisan affiliations. Building on the belief gap hypothesis, this study adopts a nationally representative survey (N = 1,119) to examine whether political identity or support for Trump is more strongly related to having false beliefs about COVID-19. Results showed that support for Trump is a better predictor of having false beliefs about COVID-19 than conservative/Republican political identity. Support for Trump predicted having false beliefs, and such a tendency increased when they were more educated. Trust in scientific and news media institutions and conservative news use mediated the relationship between support for Trump and having false beliefs. Our findings bear implications on belief gap studies by introducing new mediators such as different dimensions of institutional trust and shed light on why people who support Trump are more susceptible to false claims about COVID-19.

Acknowledgment

The authors acknowledge the support of the Good Systems Grand Challenge Research effort at the University of Texas at Austin. This paper is a project of the University of Texas at Austin’s Digital Media Research Program. We thank the anonymous reviewers for their insightful comments on an earlier draft of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15205436.2022.2144380

Notes

¹ However, this study acknowledges some cases where ideology and group identity do not overlap or exert the same influence on people’s beliefs or attitudes (Van Bavel & Pereira, 2018); Veenstra et al. (2014) found that ideology and party affiliation are independently related to people’s beliefs.

² We conducted additional analyses where ideology and party affiliation are regarded as two different predictors for false beliefs (online Appendix G). The results were not different from the analysis where we combined ideology and affiliation into one independent variable.

³ For instance, while the Republican party’s position toward immigration was “to encourage illegal aliens to return home voluntarily,” Trump took an extreme position that undocumented immigrants should be deported. See Bump (2015) for other disparities in issue positions between the party and Trump.

⁴ In this study, Congress, courts, state and city government, and police are labeled public institutions. Some researchers distinguish between trust in less political branches (e.g., police, courts) from more political institutions (e.g., government, Congress) (Rothsetein & Stolle, 2008): we did not make the distinction because of how trust varies within different public institutions is not directly linked to our hypotheses. We discussed the rationale and limitations of using such operationalization in the following sections.

⁵ Responses with a duplicate IP address (n = 19), incomplete responses (n = 162), responses that failed at least one of the two attention check questions (n = 811), responses from people younger than 18 years old (n = 100), or responses that were completed in less than 5.1 minutes (n = 32) were dropped.

⁶ We looked at issue positions rather than identity when measuring ideology; the way we measured ideology already has social group implications as the measurements involve placing oneself on the left-right scale. This provides another rationale for combining ideology and party affiliation.

⁷ The result showed two factors accounting for 66.70% of the variance. Factor 1 is labeled as trust in scientific institutions, and factor 2 is labeled as trust in public institutions (See online Appendix B).

⁸ The news media trust measurement is different from scientific and public institutions because media and political scholars worked independently on measures of trust. Those who created the survey in this study was based on used measures typically used for news media and institutional trust. Our measure provides a more nuanced operationalization of news media trust.

⁹ These news outlets were selected based on each outlet’s average audience’s party and ideology placement (Jurkowitz et al., 2020) and target viewers (Mitchell et al., 2020).

¹⁰ The internal consistency measure is unnecessary because the items present the number of incorrect answers and are “not the underlying concept” (Hindman, 2012).

¹¹ See online Appendix C for the results of using the two measures versus the other three.

Additional information

Notes on contributors

Gyo Hyun Koo

Gyo Hyun Koo (M.A., Indiana University Bloomington) is a Ph.D. candidate in the School of Journalism and Media at the University of Texas at Austin. Her research interests include how individuals perceive, select, and engage with online information and news. (https://orcid.org/0000-0002-3188-4588)

Thomas J. Johnson

Thomas J. Johnson, Digital Media Research Program Director, is the Amon G. Carter Jr. Centennial Professor in the School of Journalism and Media. His research has focused on the uses and effects of new media in politics.

Taeyoung Lee

Taeyoung Lee (M.A., Indiana University Bloomington) is a Ph.D. candidate in the School of Journalism and Media at The University of Texas at Austin. (https://orcid.org/0000-0002-1657-678X).

Chenyan Jia

Chenyan Jia (Ph.D., University of Texas at Austin) is a postdoctoral scholar in The Program on Democracy and the Internet (PDI) at Stanford University. Her research interests include algorithmic bias, computational social media, and human-computer interaction. (https://orcid.org/0000-0002-8407-9224).