Dynamic Effects of Falsehoods and Corrections on Social Media: A Theoretical Modeling and Empirical Evidence

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.