News Sharing, Persuasion and a Theory of Misinformation Spreading in Social Networks

In this research, we study models of online news dissemination on a Twitter-like social network. Given a noisy observation of the state of the world henceforth called the news, agents with heterogeneous priors decide whether to share with their followers based on whether receiving the news can persuade their followers to move their beliefs closer to theirs in aggregate.

A set of four alphabet blocks on a table with the first two showing the letters F and A; and the last two being turned from one side to another, going from KE to CT (making the word change from fake to fact)

We demonstrate how surprise and affirmationmotives naturally emerge from the utility-maximizing behavior of agents. We fully characterize thedynamics of the news spread and uncover the mechanisms that lead to a sharing cascade. We furtherinvestigate the impact of the network structure, heterogeneity of priors, and precision levels of news onthe ex-ante probability of the news going viral. In particular, we show that as individual perspectivesbecome more diverse, a wider range of news precision levels cause a cascade. Finally, we elucidatean association between the news precision levels that maximize the probability of a cascade and theprior wisdom of the crowd. Our results complement the empirical findings that support wider spreadof inaccurate/false news compared to accurate information on social networks, providing a theoreticalmicro-foundation for utility-based news-sharing decisions.

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Artificial Intelligence


Data Science

Info & Decision

Institutional Behavior


Big Data

Social Networks

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