Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic: A Structural Topic Modeling Approach
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Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic : A Structural Topic Modeling Approach. / Lu, Jiahui; Liu, Jun.
2022. Abstract from Media and Publics.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
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TY - ABST
T1 - Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic
T2 - Media and Publics
AU - Lu, Jiahui
AU - Liu, Jun
PY - 2022
Y1 - 2022
N2 - Ethnic and racial disparities in the current coronavirus (COVID-19) pandemic raise significantconcerns. This study analyzes social media discourses toward four ethnic communities in the USduring the pandemic and reveals disparities in pandemic experiences among them. A total of488,029 tweets mentioning one of four ethnic communities, i.e. Asians, Blacks, Hispanics, andNative Americans, were investigated by a structural topic modeling approach with emotionalexpressions and time as covariates in the topic model. The results demonstrate that discoursesabout Asian, Hispanics, and Native American communities were often induced by pandemic-related events, concerning topics beyond one’s community, and reflecting an experience of implicitracism and an adoption of technical supports from health systems. Meanwhile, discourses aboutBlacks were racially-related, discussing topics within the community, and reflecting an experienceof explicit racism and an adoption of psychological supports from ingroup. We discuss theimplications of our findings on ethnic health disparities
AB - Ethnic and racial disparities in the current coronavirus (COVID-19) pandemic raise significantconcerns. This study analyzes social media discourses toward four ethnic communities in the USduring the pandemic and reveals disparities in pandemic experiences among them. A total of488,029 tweets mentioning one of four ethnic communities, i.e. Asians, Blacks, Hispanics, andNative Americans, were investigated by a structural topic modeling approach with emotionalexpressions and time as covariates in the topic model. The results demonstrate that discoursesabout Asian, Hispanics, and Native American communities were often induced by pandemic-related events, concerning topics beyond one’s community, and reflecting an experience of implicitracism and an adoption of technical supports from health systems. Meanwhile, discourses aboutBlacks were racially-related, discussing topics within the community, and reflecting an experienceof explicit racism and an adoption of psychological supports from ingroup. We discuss theimplications of our findings on ethnic health disparities
M3 - Conference abstract for conference
Y2 - 28 April 2022 through 29 April 2022
ER -
ID: 291607913