Wednesday, March 10, 2021 - 4:00pm to 4:30pm
Event Calendar Category
LIDS & Stats Tea
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912 9633 7492
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We present a model of online content sharing where agents sequentially observe an article and must decide whether to share it with others. The article may contain misinformation, but at a cost, agents can “fact-check” to determine whether its content is entirely accurate. While agents derive value from future shares, they simultaneously fear getting caught sharing misinformation. With little homophily in the social network, misinformation is often quickly identified and brought to an end. However, when homophily is strong, whereby agents anticipate that only those with similar beliefs will view the article, misinformation spreads more rapidly because of echo chambers. We use this to show that a social media platform that wants to maximize content engagement should propagate extreme articles amongst the most extremist users, while not showing these articles to ideologically-opposed users. This creates an endogenous echo chamber, or “filter bubble,” that is highly conducive to viral misinformation. Policies that reveal an article’s provenance or censor extreme articles can often encourage more fact-checking by online users and mitigate the consequences of filter bubbles.
James is a fifth-year PhD candidate in LIDS advised by Asu Ozdaglar and Daron Acemoglu. His focus of research includes network models and learning with specific applications to game theory, economics, and finance, and a particular focus on systemic risk in endogenous financial networks and the spread of misinformation in social networks.