Exploring how online responses change in response to debunking messages about COVID-19 on WhatsApp

• Main Authors: CHEN, Xingyu Ken, NA, Jin-Cheon, TAN, Luke Kien-Weng, CHONG, Mark, CHOY, Murphy
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2022
• 主題: COVID-19; Debunking; Aspect-based sentiment analysis; Information distortion; Source credibility; Deep learning; Asian Studies; Health Communication; Public Health; Social Media
• 在線閱讀: https://ink.library.smu.edu.sg/lkcsb_research/6973; https://ink.library.smu.edu.sg/context/lkcsb_research/article/7972/viewcontent/DebunkingMessagesAboutCOVID_2022_av.pdf
• 機構: Singapore Management University

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...