ABSTRACT:
Although government agencies and fact-checking organizations issue correction messages to mitigate misinformation dissemination on social media, their efforts have been largely ineffective. Drawing on Information Manipulation Theory (IMT) and information extracted from over 84 million tweets, we examine the impact of correction messages and explore how different information updates affect their effectiveness. Our empirical approach differentiates between subpopulations of correction messages. The results support the existence of two distinct types: correction messages that significantly mitigate the spread of fake news—a 1 percent increase in “effective” messages decreases the spread of fake news by 0.89 percent; and those that amplify the misinformation—a 1 percent increase in “ineffective” messages increases the spread of fake news by 1.37 percent. Despite the higher count of “effective” correction messages, they are less potent than their “ineffective” counterparts. Our study extends the tenets of IMT to correction messages to identify effective platform interventions and highlight the efficacy of information updates in reducing the spread of fake news. Furthermore, it offers significant insights to social media platforms and fact-checking organizations on designing and deploying effective corrections.
Key words and phrases: Social media, fake news, news corrections, message updates, information manipulation, onine platforms