Detecting Social Media Rumor Debunking Effectiveness During Public Health Emergencies: An Interpretable Machine Learning Approach

Shuai Zhang*, Jianhua Hou, Yang Zhang*, Zhizhen Yao, Zhijian Zhang

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Citation (Scopus)

Abstract

Debunking offers a promising approach to counteracting social media rumors during public health emergencies. However, the effective mechanisms of rumor debunking on social media remain unverified. This study employs an interpretable machine learning approach, combined with information and communication theories, to investigate social media rumor debunking effectiveness and its influencing factors. A total of 10,150 COVID-19 rumor-debunking posts and other relevant data on Sina Weibo were collected for analysis. The results showed that the beneficial impacts of debunking rumors surpass the adverse consequences and revealed significant differences in debunking effectiveness across diverse rumor types, topics, and involvement levels. © The Author(s) 2024.
Original languageEnglish
Pages (from-to)23–56
JournalScience Communication
Volume47
Issue number1
Online published27 Jul 2024
DOIs
Publication statusPublished - Feb 2025

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

  • debunking effectiveness
  • interpretable machine learning
  • public health emergencies
  • rumors
  • social media

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