Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning
-
- Xiaoling Xiang
- School of Social Work, University of Michigan, Ann Arbor
-
- Xuan Lu
- School of Information, University of Michigan, Ann Arbor
-
- Alex Halavanau
- SLAC National Accelerator Laboratory, Menlo Park, California
-
- Jia Xue
- Factor-Inwentash Faculty of Social Work and the Faculty of Information, University of Toronto, Ontario, Canada
-
- Yihang Sun
- School of Social Work, University of Michigan, Ann Arbor
-
- Patrick Ho Lam Lai
- School of Social Work, University of Michigan, Ann Arbor
-
- Zhenke Wu
- School of Public Health, University of Michigan, Ann Arbor
-
- Deborah S Carr
- editor
Abstract
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objectives</jats:title><jats:p>This study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Twitter data (N = 82,893) related to both older adults and COVID-19 and dated from January 23 to May 20, 2020, were analyzed. We used a combination of data science methods (including supervised machine learning, topic modeling, and sentiment analysis), qualitative thematic analysis, and conventional statistics.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). The daily average of ageist content was 18%, with the highest of 52.8% on March 11, 2020. Specifically, more than 1 in 10 (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” Also, 14 themes were extracted, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>Ageist content in the context of COVID-19 was prevalent on Twitter. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.</jats:p></jats:sec>
Journal
-
- The Journals of Gerontology: Series B
-
The Journals of Gerontology: Series B 76 (4), e190-e200, 2020-08-12
Oxford University Press (OUP)
- Tweet
Details 詳細情報について
-
- CRID
- 1360861295053366016
-
- ISSN
- 17585368
- 10795014
-
- Data Source
-
- Crossref