Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic: A Structural Topic Modeling Approach

Research output: Contribution to conference › Conference abstract for conference › Research › peer-review

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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 The 72nd Annual International Communication Association Conference.

Research output: Contribution to conference › Conference abstract for conference › Research › peer-review

Harvard

Lu, J & Liu, J 2022, 'Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic: A Structural Topic Modeling Approach', The 72nd Annual International Communication Association Conference, 26/05/2022 - 30/05/2022.

APA

Lu, J., & Liu, J. (2022). Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic: A Structural Topic Modeling Approach. Abstract from The 72nd Annual International Communication Association Conference.

Vancouver

Lu J, Liu J. Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic: A Structural Topic Modeling Approach. 2022. Abstract from The 72nd Annual International Communication Association Conference.

Author

Lu, Jiahui ; Liu, Jun. / Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic : A Structural Topic Modeling Approach. Abstract from The 72nd Annual International Communication Association Conference.

Bibtex

@conference{7d44c1f3ca7142768f08b9ac2d1a9817,
title = "Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic: A Structural Topic Modeling Approach",
abstract = "Ethnic and racial disparities in the current coronavirus (COVID-19) pandemic raise significant concerns. This study analyzes social media discourses toward four ethnic communities in the US during the pandemic and reveals disparities in pandemic experiences among them. A total of 488,029 tweets mentioning one of four ethnic communities, i.e. Asians, Blacks, Hispanics, and Native Americans, were investigated by a structural topic modeling approach with emotional expressions and time as covariates in the topic model. The results demonstrate that discourses about Asian, Hispanics, and Native American communities were often induced by pandemic-related events, concerning topics beyond one{\textquoteright}s community, and reflecting an experience of implicit racism and an adoption of technical supports from health systems. Meanwhile, discourses about Blacks were racially-related, discussing topics within the community, and reflecting an experience of explicit racism and an adoption of psychological supports from ingroup. We discuss the implications of our findings on ethnic health disparities.",
author = "Jiahui Lu and Jun Liu",
year = "2022",
language = "English",
note = "The 72nd Annual International Communication Association Conference ; Conference date: 26-05-2022 Through 30-05-2022",

}

RIS

TY - ABST

T1 - Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media during the COVID-19 Pandemic

T2 - The 72nd Annual International Communication Association Conference

AU - Lu, Jiahui

AU - Liu, Jun

PY - 2022

Y1 - 2022

N2 - Ethnic and racial disparities in the current coronavirus (COVID-19) pandemic raise significant concerns. This study analyzes social media discourses toward four ethnic communities in the US during the pandemic and reveals disparities in pandemic experiences among them. A total of 488,029 tweets mentioning one of four ethnic communities, i.e. Asians, Blacks, Hispanics, and Native Americans, were investigated by a structural topic modeling approach with emotional expressions and time as covariates in the topic model. The results demonstrate that discourses about Asian, Hispanics, and Native American communities were often induced by pandemic-related events, concerning topics beyond one’s community, and reflecting an experience of implicit racism and an adoption of technical supports from health systems. Meanwhile, discourses about Blacks were racially-related, discussing topics within the community, and reflecting an experience of explicit racism and an adoption of psychological supports from ingroup. We discuss the implications of our findings on ethnic health disparities.

AB - Ethnic and racial disparities in the current coronavirus (COVID-19) pandemic raise significant concerns. This study analyzes social media discourses toward four ethnic communities in the US during the pandemic and reveals disparities in pandemic experiences among them. A total of 488,029 tweets mentioning one of four ethnic communities, i.e. Asians, Blacks, Hispanics, and Native Americans, were investigated by a structural topic modeling approach with emotional expressions and time as covariates in the topic model. The results demonstrate that discourses about Asian, Hispanics, and Native American communities were often induced by pandemic-related events, concerning topics beyond one’s community, and reflecting an experience of implicit racism and an adoption of technical supports from health systems. Meanwhile, discourses about Blacks were racially-related, discussing topics within the community, and reflecting an experience of explicit racism and an adoption of psychological supports from ingroup. We discuss the implications of our findings on ethnic health disparities.

M3 - Conference abstract for conference

Y2 - 26 May 2022 through 30 May 2022

ER -

ID: 291608397