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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

We investigated the content of liberal and conservative news media Facebook posts on race and ethnic health disparities. A total of 3,327,360 liberal and conservative news Facebook posts from the United States (US) from January 2015 to May 2022 were collected from the Crowd Tangle platform and filtered for race and health-related keywords. Qualitative content analysis was conducted on a random sample of 1750 liberal and 1750 conservative posts. Posts were analyzed for a continuum of hate speech using a newly developed method combining faceted Rasch item response theory with deep learning. Across posts referencing Asian, Black, Latinx, Middle Eastern, and immigrants/refugees, liberal news posts had lower hate scores compared to conservative posts. Liberal news posts were more likely to acknowledge and detail the existence of racial/ethnic health disparities, while conservative news posts were more likely to highlight the negative consequences of protests, immigration, and the disenfranchisement of Whites. Facebook posts from liberal and conservative news focus on different themes with fewer discussions of racial inequities in conservative news. Investigating the discourse on race and health in social media news posts may inform our understanding of the public’s exposure to and knowledge of racial health disparities, and policy-level support for ameliorating these disparities.

Details

Title
Examining Exposure to Messaging, Content, and Hate Speech from Partisan News Social Media Posts on Racial and Ethnic Health Disparities
Author
Nguyen, Thu T 1   VIAFID ORCID Logo  ; Yu, Weijun 1   VIAFID ORCID Logo  ; Merchant, Junaid S 1 ; Criss, Shaniece 2   VIAFID ORCID Logo  ; Kennedy, Chris J 3   VIAFID ORCID Logo  ; Mane, Heran 1 ; Gowda, Krishik N 1 ; Kim, Melanie 4 ; Belani, Ritu 1   VIAFID ORCID Logo  ; Blanco, Caitlin F 1   VIAFID ORCID Logo  ; Kalachagari, Manvitha 1 ; Yue, Xiaohe 1 ; Volpe, Vanessa V 5   VIAFID ORCID Logo  ; Allen, Amani M 6 ; Hswen, Yulin 7 ; Nguyen, Quynh C 1   VIAFID ORCID Logo 

 Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA 
 Department of Health Sciences, Furman University, Greenville, SC 29613, USA 
 Department of Psychiatry, Harvard Medical School, Boston, MA 02114, USA 
 Department of Anthropology, Brown University, Providence, RI 02912, USA 
 Department of Psychology, North Carolina State University, Raleigh, NC 27695, USA 
 Divisions of Community Health Sciences and Epidemiology, University of California, Berkeley, CA 94704, USA 
 Department of Epidemiology and Biostatistics, Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143, USA 
First page
3230
Publication year
2023
Publication date
2023
Publisher
MDPI AG
ISSN
1661-7827
e-ISSN
1660-4601
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2779510346
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.