谣言
社会化媒体
公共卫生
数据科学
心理学
人工智能
公共关系
计算机科学
社会学
机器学习
政治学
医学
万维网
护理部
作者
Shuai Zhang,Jianhua Hou,Yang Zhang,Zhizhen Yao,Zhijian Zhang
标识
DOI:10.1177/10755470241261323
摘要
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.
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