School of Management, Harbin Institute of Technology, Harbin, China.
Front Public Health. 2021 Nov 30;9:770111. doi: 10.3389/fpubh.2021.770111. eCollection 2021.
The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media. A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index () was developed to measure the effectiveness of different debunking methods. The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of rumor and political category of rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method. The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.
社交媒体上与 COVID-19 相关谣言的传播给公共卫生治理带来了巨大挑战,因此,迅速有效地揭露谣言并遏制其传播已成为当务之急。本研究旨在协助制定有效策略,揭露并遏制社交媒体上的谣言传播。
本研究共随机选取了 2053 个原始帖子和 100348 条回复帖子的评论,这些回复针对的是五个与 COVID-19 相关的虚假谣言(日期为 2020 年 1 月 20 日至 2020 年 6 月 28 日),这些谣言分别属于权威、社会和政治三个类别,它们都发布在中国的新浪微博上。为了研究不同辟谣方法的有效性,本研究提出了一种新的注释方案,将辟谣方法分为六种类型:否认、进一步事实核查、反驳、回应人、回应组织和组合方法。本研究使用深度学习方法构建了文本分类器,自动识别回复辟谣帖子的四种用户立场:支持、否认、质疑和评论立场。然后,基于立场回复,本研究开发了一种辟谣有效性指数(),以衡量不同辟谣方法的有效性。
具有引用证据的反驳方法无论单独使用还是与其他辟谣方法结合使用,都具有最佳的辟谣效果。对于社会类别谣言和政治类别谣言,单独使用反驳方法可以达到最佳的辟谣效果。对于权威类别谣言,组合方法具有最佳的辟谣效果,但最有效的组合方法要求避免使用同时包含被权威谣言中诽谤的人或组织亲自回应和反驳方法的组合。
这些发现为有效辟谣提供了相关见解,支持虚假信息危机管理,并在公共卫生紧急情况下对谣言采取必要行动。