A Theory of Misinformation Spread on Social Networks

We study a strategic model of online news dissemination on a Twitter-like social network. Agents with heterogeneous priors decide whether to forward a piece of news they received to their followers. Each agent makes a forwarding decision based on whether the news can persuade the followers to think more like them in aggregate. At the micro-level, we show how novelty and affirmation motives naturally emerge from the utility-maximizing behavior of the agents when persuasion is the main motive for sharing news. We characterize the dynamics of the news spread and establish the equation governing the steady state size of news cascades. Exact necessary and sufficient conditions are derived for emergence of a cascade, based on which we formulate the problem of finding the news precision level maximizing ex-ante likelihood of a sharing cascade. We show that if the cost associated with broadcasting to followers is sufficiently small, then a cascade occurs almost surely for news that has enough accuracy. When the cost associated with broadcasting passes a certain threshold, the optimal precision needed for a cascade is related to the aggregate wisdom of the crowd, and more precisely, to whether the aggregation of agents' prior beliefs concentrate on the truth. When the society as a whole is biased, ie, there is a gap between the true state and the aggregation of prior perspectives, the truth almost always triggers a cascade. In contrast, in a wise or unbiased society, cascades are more likely to occur for false news, ie, information that is likely to be inaccurate. Our results complement the empirical findings that support wider spread of …