Project: News sharing, persuasion and a theory of misinformation spreading in social networks

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.

We demonstrate how surprise and affirmation motives naturally emerge from the utility-maximizing behavior of agents. We fully characterize the dynamics of the news spread and uncover the mechanisms that lead to a sharing cascade. We further investigate the impact of the network structure, heterogeneity of priors, and precision levels of news on the ex-ante probability of the news going viral. In particular, we show that as individual perspectives become more diverse, a wider range of news precision levels cause a cascade. Finally, we elucidate an association between the news precision levels that maximize the probability of a cascade and the prior wisdom of the crowd. Our results complement the empirical findings that support wider spread of inaccurate/false news compared to accurate information on social networks, providing a theoretical micro-foundation for utility-based news-sharing decisions.


Artificial Intelligence


Data Science

Info & Decision

Institutional Behavior


Big Data

Social Networks

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