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

Abstract

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.more » 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.« less

Authors:
ORCiD logo [1];  [1];  [2];  [3];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Univ. of Michigan, Ann Arbor, MI (United States)
  2. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  3. Univ. of Toronto, ON (Canada)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1810565
Grant/Contract Number:  
AC02-76SF00515
Resource Type:
Accepted Manuscript
Journal Name:
The Journals of Gerontology: Series B
Additional Journal Information:
Journal Volume: 76; Journal Issue: 4; Journal ID: ISSN 1079-5014
Country of Publication:
United States
Language:
English
Subject:
ageism; COVID-19; machine learning; social media; Twitter; older adult; pandemics; discourse

Citation Formats

Xiang, Xiaoling, Lu, Xuan, Halavanau, Alex, Xue, Jia, Sun, Yihang, Lai, Patrick Lam, and Wu, Zhenke. Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning. United States: N. p., 2020. Web. https://doi.org/10.1093/geronb/gbaa128.
Xiang, Xiaoling, Lu, Xuan, Halavanau, Alex, Xue, Jia, Sun, Yihang, Lai, Patrick Lam, & Wu, Zhenke. Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning. United States. https://doi.org/10.1093/geronb/gbaa128
Xiang, Xiaoling, Lu, Xuan, Halavanau, Alex, Xue, Jia, Sun, Yihang, Lai, Patrick Lam, and Wu, Zhenke. Wed . "Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning". United States. https://doi.org/10.1093/geronb/gbaa128. https://www.osti.gov/servlets/purl/1810565.
@article{osti_1810565,
title = {Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning},
author = {Xiang, Xiaoling and Lu, Xuan and Halavanau, Alex and Xue, Jia and Sun, Yihang and Lai, Patrick Lam and Wu, Zhenke},
abstractNote = {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.},
doi = {10.1093/geronb/gbaa128},
journal = {The Journals of Gerontology: Series B},
number = 4,
volume = 76,
place = {United States},
year = {2020},
month = {8}
}

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