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

     Authors

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

Publication type

Article - research, tables/charts

Date of Publication

12 August 2020

Series/Report no.

The Journals of Gerontology: Series B, Volume 76, Issue 4

Key words

Ageism, COVID-19, machine learning, social media, Twitter

COVID19- Healthcare Challenges for Older Adults

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.

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. Public education on the risk of severe illness is needed to correct misperceptions.

주요 내용

이 연구는 2020 년 코로나 상황 발생 이후 트위터에서 COVID-19 노인에 대한 공개 담론과 감정에 대한 최초의 대규모 분석을 제공했습니다. 본 연구의 분석 방식에는 연령을 연구하기 위한 인공 지능 방법의 새로운 분석법이 포함되었습니다. 질적 주제 분석 및 전통적인 통계 방법과 함께 해당 방법을 적용하면 COVID-19 상황에서 노인에 대한 대중의 태도를 계속 추적하고 잠재적으로 효과적인 연령 차별을 분석할 수 있습니다. 

세 가지 분석 방식의 트윗 속성을 모두 결합한 분석 결과, 데이터에 포함된 트윗의 16.4 %가 연령 차별적 콘텐츠를 포함하고 있으며 대부분은 노령의 요소를 암시하였습니다.

연령별 콘텐츠의 일일 평균은 18 %로 2020 년 3 월 11 일에 52.8 %로 가장 높았습니다. 세계 보건기구 (WHO)가 COVID-19를 pandemic이라고 선언했을 때와 일치했습니다. 

ISBN

1079-5014

DOI

https://doi.org/10.1093/geronb/gbaa128