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Optimal Signaling of Content Accuracy: Engagement vs. Misinformation

Ozan Candogan () and Kimon Drakopoulos ()
Additional contact information
Ozan Candogan: Booth School of Business, University of Chicago, Chicago, Illinois 60637
Kimon Drakopoulos: Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90089

Operations Research, 2020, vol. 68, issue 2, 497-515

Abstract: This paper studies information design in social networks. We consider a setting, where agents’ actions exhibit positive local network externalities. There is uncertainty about the underlying state of the world, which impacts agents’ payoffs. The platform can commit to a signaling mechanism that sends informative signals to agents upon realization of this uncertainty, thereby influencing their actions. Although this abstract setting has many applications, we discuss our results in the context of a specific one: A platform can send informative signals to agents in a social network to influence their engagement decisions with the available content, based on the realization of the inaccuracy of the content. We investigate how the platform should design its signaling mechanism to maximize engagement/minimize misinformation. The optimal (in terms of engagement/misinformation) signaling mechanism admits a simple threshold structure: The platform recommends that agents “engage” with the content if its inaccuracy level is below a threshold and recommends “do not engage” otherwise. For the mechanism that maximizes engagement, these thresholds depend on agents’ network positions, which we capture through a novel centrality measure. In the case where the platform seeks only to minimize misinformation (regardless of the induced engagement), common threshold mechanisms with identical thresholds across agents are optimal. This is in contrast to the engagement maximization setting, where when agents are heterogeneous in terms of their network positions, common threshold mechanisms induce substantially lower engagement than the optimal mechanisms. We also study the frontier of the engagement/misinformation levels that can be achieved via different mechanisms and characterize when common threshold mechanisms achieve optimal trade-offs. Finally, we supplement our theoretical findings with numerical simulations on a Facebook subgraph.

Keywords: social networks; information design; misinformation; fake news; online platforms (search for similar items in EconPapers)
Date: 2020
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