ABSTRACT

Government agencies and fact-checking websites have been combating the spread of falsehoods on social media by issuing correction messages. There has been, however, no research on the effectiveness of correction messages on falsehoods and their dynamic interaction. We develop a theoretical model of the competition between falsehoods and correction messages on Twitter and show different interventions under which falsehoods could be hampered. Moreover, we use panel vector autoregressive models and machine learning techniques to empirically investigate the dynamic interactions between falsehoods and correction messages through a unique longitudinal dataset of 279,597 tweets. We find that correction messages cause an increase in the propagation of falsehoods on social media if their use is not optimized. This study highlights the importance of having government agencies, fact-checking websites, and social media platforms work together to optimize effective correction messages. We argue such an effort will counter the spread of falsehoods.

摘要

政府机构和事实核查网站一直在通过发布更正信息来打击虚假信息在社交媒体上的传播。然而，还没有关于更正信息对虚假信息及其动态相互作用的有效性的研究。我们开发了 Twitter 上虚假信息和更正消息之间竞争的理论模型，并展示了可以阻止虚假信息的不同干预措施。此外，我们使用面板向量自回归模型和机器学习技术，通过 279,597 条推文的独特纵向数据集，凭经验研究虚假信息和更正消息之间的动态相互作用。我们发现，如果不优化使用，更正消息会导致虚假信息在社交媒体上的传播增加。这项研究强调了让政府机构、事实核查网站和社交媒体平台合作以优化有效更正信息的重要性。我们认为，这样的努力将对抗谎言的传播。