News Sharing, Persuasion, and Spread of Misinformation on Social Networks

45 Pages Posted: 10 Jun 2019 Last revised: 1 Jul 2020

See all articles by Chin-Chia Hsu

Chin-Chia Hsu

Institute for Data, Systems, and Society, Massachusetts Institute of Technology

Amir Ajorlou

Massachusetts Institute of Technology - Laboratory for Information and Decision Systems

Ali Jadbabaie

Institute for Data, Systems, and Society, Massachusetts Institute of Technology

Date Written: July 1, 2020

Abstract

In this paper, we study a model 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.

Keywords: Spread of Misinformation, Persuasion, Information Disclosure, Social Networks, Collective Wisdom

JEL Classification: D01, D03, D82, D83

Suggested Citation

Hsu, Chin-Chia and Ajorlou, Amir and Jadbabaie, Ali, News Sharing, Persuasion, and Spread of Misinformation on Social Networks (July 1, 2020). Available at SSRN: https://ssrn.com/abstract=3391585 or http://dx.doi.org/10.2139/ssrn.3391585

Chin-Chia Hsu (Contact Author)

Institute for Data, Systems, and Society, Massachusetts Institute of Technology ( email )

77 Massachusetts Ave E17-442
Cambridge, MA 02139
United States
6179490669 (Phone)

HOME PAGE: http://chinchia.mit.edu

Amir Ajorlou

Massachusetts Institute of Technology - Laboratory for Information and Decision Systems ( email )

E32-D569, 32 Vassar Street,
Cambridge, MA 02139
United States
215-919-3234 (Phone)

HOME PAGE: http://www.mit.edu/~ajorlou

Ali Jadbabaie

Institute for Data, Systems, and Society, Massachusetts Institute of Technology ( email )

77 Massachusetts Ave E18-309C
E18-309C
02139, MA MA 02139
United States
6172537339 (Phone)
6172537339 (Fax)

HOME PAGE: http://web.mit.edu/www/jadbabai

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