Measuring Daily-life Fear Perception Change: A Computational Study in the Context of COVID-19

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2022 Dec 22

PMID 36548306

Authors

Yuchen Chai

Juan Palacios

Jianghao Wang

Yichun Fan

Siqi Zheng

Affiliations

Soon will be listed here.

Abstract

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.

Citing Articles

Examining hurricane-related social media topics longitudinally and at scale: A transformer-based approach.

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PMID: 39854483 PMC: 11760010. DOI: 10.1371/journal.pone.0316852.

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