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Politics Go "Viral": A Computational Text Analysis of the Public Attribution and Attitude Regarding the COVID-19 Crisis and Governmental Responses on Twitter
Social Science Computer Review ; 41(3):790-811, 2023.
Article in English | Academic Search Complete | ID: covidwho-20245295
ABSTRACT
The U.S. confronts an unprecedented public health crisis, the COVID-19 pandemic, in the presidential election year in 2020. In such a compound situation, a real-time dynamic examination of how the general public ascribe the crisis responsibilities taking account to their political ideologies is helpful for developing effective strategies to manage the crisis and diminish hostility toward particular groups caused by polarization. Social media, such as Twitter, provide platforms for the public's COVID-related discourse to form, accumulate, and visibly present. Meanwhile, those features also make social media a window to monitor the public responses in real-time. This research conducted a computational text analysis of 2,918,376 tweets sent by 829,686 different U.S. users regarding COVID-19 from January 24 to May 25, 2020. Results indicate that the public's crisis attribution and attitude toward governmental crisis responses are driven by their political identities. One crisis factor identified by this study (i.e., threat level) also affects the public's attribution and attitude polarization. Additionally, we note that pandemic fatigue was identified in our findings as early as in March 2020. This study has theoretical, practical, and methodological implications informing further health communication in a heated political environment. [ FROM AUTHOR] Copyright of Social Science Computer Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Social Science Computer Review Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Social Science Computer Review Year: 2023 Document Type: Article