Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2022

Abstract

Purpose: The COVID-19 pandemic has spurred a concurrent outbreak of false information online. Debunking false information about a health crisis is critical as misinformation can trigger protests or panic, which necessitates a better understanding of it. This exploratory study examined the effects of debunking messages on a COVID-19-related public chat on WhatsApp in Singapore. Design/methodology/approach: To understand the effects of debunking messages about COVID-19 on WhatsApp conversations, the following was studied. The relationship between source credibility (i.e. characteristics of a communicator that affect the receiver's acceptance of the message) of different debunking message types and their effects on the length of the conversation, sentiments towards various aspects of a crisis, and the information distortions in a message thread were studied. Deep learning techniques, knowledge graphs (KG), and content analyses were used to perform aspect-based sentiment analysis (ABSA) of the messages and measure information distortion. Findings: Debunking messages with higher source credibility (e.g. providing evidence from authoritative sources like health authorities) help close a discussion thread earlier. Shifts in sentiments towards some aspects of the crisis highlight the value of ABSA in monitoring the effectiveness of debunking messages. Finally, debunking messages with lower source credibility (e.g. stating that the information is false without any substantiation) are likely to increase information distortion in conversation threads. Originality/value: The study supports the importance of source credibility in debunking and an ABSA approach in analysing the effect of debunking messages during a health crisis, which have practical value for public agencies during a health crisis. Studying differences in the source credibility of debunking messages on WhatsApp is a novel shift from the existing approaches. Additionally, a novel approach to measuring information distortion using KGs was used to shed insights on how debunking can reduce information distortions.

Keywords

COVID-19, Debunking, Aspect-based sentiment analysis, Information distortion, Source credibility, Deep learning

Discipline

Asian Studies | Health Communication | Public Health | Social Media

Research Areas

Corporate Communication

Publication

Online Information Review

Volume

46

Issue

6

First Page

1184

Last Page

1204

ISSN

1468-4527

Identifier

10.1108/OIR-08-2021-0422

Publisher

Emerald

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1108/OIR-08-2021-0422

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