Analyzing the Antecedents of Fake News Sharing in Online Social Networks

The rapid development of artificial intelligence and the increase in the number of fake news spread on online social networks pose a global problem with harmful consequences. This study aimed to analyze the influence of various variables on the distribution of fake news on social media. The study comprised 275 participants and we analyzed the data using Partial Least Squares—Structural Equation Modeling (PLS-SEM). The study found that factors such as pass time, information sharing, social media fatigue, and self-disclosure play an important role in the distribution of fake news among users. However, altruism, socialization, information seeking, and online trust do not influence the distribution of fake news. The results have important implications for researchers, social media users, businesses, organizations, and governments, as fake news can affect any domain. Therefore, it is recommended to develop algorithms and technologies used by social media platforms to detect and remove false content, and to create educational programs to teach users to identify and report fake news.