Examining Exposure to Messaging, Content, and Hate Speech from Partisan News Social Media Posts on Racial and Ethnic Health Disparities

Autor: Thu T. Nguyen, Weijun Yu, Junaid S. Merchant, Shaniece Criss, Chris J. Kennedy, Heran Mane, Krishik N. Gowda, Melanie Kim, Ritu Belani, Caitlin F. Blanco, Manvitha Kalachagari, Xiaohe Yue, Vanessa V. Volpe, Amani M. Allen, Yulin Hswen, Quynh C. Nguyen
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: International Journal of Environmental Research and Public Health
Volume 20
Issue 4
Pages: 3230
International journal of environmental research and public health, vol 20, iss 4
ISSN: 1660-4601
DOI: 10.3390/ijerph20043230
Popis: 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.
Databáze: OpenAIRE