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, Xuan Lu, Alex Halavanau, Jia Xue, Yihang Sun, Patrick Ho Lam Lai, Zhenke Wu and Deborah S Carr

The Journals of Gerontology: Series B, 2021, vol. 76, issue 4, e190-e200

Abstract: ObjectivesThis study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse.MethodsTwitter 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.ResultsThe 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.DiscussionAgeist 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.

Keywords: Ageism; COVID-19; machine learning; social media; Twitter (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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