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We study optimal manipulation of a Bayesian learner through adaptive provisioning of information. The problem is motivated by settings in which a firm can disseminate possibly biased information at a […]
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We study the spread of misinformation in a social network characterized by unequal access to learning resources. Agents use social learning to uncover an unknown state of the world, and […]
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We study the diffusion of a true and a false message (the rumor) in a social network. Upon hearing a message, individuals may believe it, disbelieve it, or debunk it […]
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We present a model of online content sharing where agents sequentially observe an article and decide whether to share it with others. This content may or may not contain misinformation. […]
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Misinformation can negatively impact people’s lives in domains ranging from health to politics. An important research goal is to understand how misinformation spreads in order to curb it. Here, we […]
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Given the amount of misinformation being circulated on social media during the COVID-19 pandemic and its potential threat to public health, it is imperative to investigate ways to hinder its […]
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RATIONALE: The escalating dissemination of health misinformation on social media platforms poses a significant threat to users’ well-being. It is imperative to identify the types of health misinformation that are […]
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Misinformation surrounding crises poses a significant challenge for public institutions. Understanding the relative effectiveness of different types of interventions to counter misinformation, and which segments of the population are most […]
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We report results from simultaneous experiments conducted in late 2022 in Belarus, Estonia, Kazakhstan, Russia and Ukraine. The experiments focus on fact-checking misinformation supportive of Russia in the Russia-Ukraine War. […]
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Why do people share misinformation on social media? In this research (N = 2,476), we show that the structure of online sharing built into social platforms is more important than individual deficits […]
