Optimal Signaling of Content Accuracy: Engagement vs. Misinformation

• 成果类型: Article
• 署名作者: Candogan, Ozan; Drakopoulos, Kimon
• 署名单位: University of Chicago; University of Southern California
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2019.1897
• 发表日期: 2020
• 页码: 497-515
• 关键词: Bayesian persuasion; INFORMATION; networks; queues

摘要

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.