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 language | English |
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Pages (from-to) | 23–56 |
Journal | Science Communication |
Volume | 47 |
Issue number | 1 |
Online published | 27 Jul 2024 |
DOIs | |
Publication status | Published - 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