Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period

Shu Rong Lim, Qin Xiang Ng, Xiaohui Xin, Yu Liang Lim, Evelyn Swee Kim Boon and Tau Ming Liew ()
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Shu Rong Lim: Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore
Qin Xiang Ng: Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore
Xiaohui Xin: Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore
Yu Liang Lim: MOH Holdings Pte Ltd., 1 Maritime Square, Singapore 099253, Singapore
Evelyn Swee Kim Boon: Department of Psychology, Singapore General Hospital, Singapore 169608, Singapore
Tau Ming Liew: Department of Psychiatry, Singapore General Hospital, Singapore 169608, Singapore

IJERPH, 2022, vol. 19, issue 21, 1-15

Abstract: Many studies have forewarned the profound emotional and psychosocial impact of the protracted COVID-19 pandemic. This study thus aimed to examine how individuals relate to suicide amid the COVID-19 pandemic from a global perspective via the public Twitter discourse around suicide and COVID-19. Original Twitter tweets from 1 February 2020 to 10 February 2021 were searched, with terms related to “COVID-19”, “suicide”, or “self-harm”. An unsupervised machine learning approach and topic modelling were used to identify topics from unique tweets, with each topic further grouped into themes using manually conducted thematic analysis by the study investigators. A total of 35,904 tweets related to suicide and COVID-19 were processed into 42 topics and six themes. The main themes were: (1) mixed reactions to COVID-19 public health policies and their presumed impact on suicide; (2) biopsychosocial impact of COVID-19 pandemic on suicide and self-harm; (3) comparing mortality rates of COVID-19, suicide, and other leading causes of death; (4) mental health support for individuals at risk of suicide; (5) reported cases and public reactions to news related to COVID-19, suicide, and homicide; and (6) figurative usage of the word suicide. The general public was generally concerned about governments’ responses as well as the perturbing effects on mental health, suicide, the economy, and at-risk populations.

Keywords: suicide; COVID-19; strain theory; machine learning; topic modelling; social media (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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