Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning

Xiang, Xiaoling; Lu, Xuan; Halavanau, Alex; Xue, Jia; Sun, Yihang; Lai, Patrick Lam; Wu, Zhenke

doi:10.1093/geronb/gbaa128

Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning

Journal Article · Wed Aug 12 00:00:00 EDT 2020 · The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences

DOI:https://doi.org/10.1093/geronb/gbaa128· OSTI ID:1810565

^[1]; Lu, Xuan ^[1]; Halavanau, Alex ^[2]; Xue, Jia ^[3]; Sun, Yihang ^[1]; ^[1]; ^[1]

Univ. of Michigan, Ann Arbor, MI (United States)
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Univ. of Toronto, ON (Canada)

Objectives 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. Methods 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. Results 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. Discussion 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. Finaly, public education on the risk of severe illness is needed to correct misperceptions.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1810565

Journal Information:: The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, Journal Name: The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences Journal Issue: 4 Vol. 76; ISSN 1079-5014

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

60 APPLIED LIFE SCIENCES
COVID-19
Twitter
ageism
discourse
machine learning
older adult
pandemics
social media

Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning

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References (16)

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