Fake News Propagation and Detection: A Sequential Model

Yiangos Papanastasiou ()
Additional contact information
Yiangos Papanastasiou: Haas School of Business, University of California, Berkeley, Berkeley, California 94720

Management Science, 2020, vol. 66, issue 5, 1826-1846

Abstract: In the wake of the 2016 U.S. presidential election, social-media platforms are facing increasing pressure to combat the propagation of “fake news” (i.e., articles whose content is fabricated). Motivated by recent attempts in this direction, we consider the problem faced by a social-media platform that is observing the sharing actions of a sequence of rational agents and is dynamically choosing whether to conduct an inspection (i.e., a “fact-check”) of an article whose validity is ex ante unknown. We first characterize the agents’ inspection and sharing actions and establish that, in the absence of any platform intervention, the agents’ news-sharing process is prone to the proliferation of fabricated content, even when the agents are intent on sharing only truthful news. We then study the platform’s inspection problem. We find that because the optimal policy is adapted to crowdsource inspection from the agents, it exhibits features that may appear a priori nonobvious; most notably, we show that the optimal inspection policy is nonmonotone in the ex ante probability that the article being shared is fake. We also investigate the effectiveness of the platform’s policy in mitigating the detrimental impact of fake news on the agents’ learning environment. We demonstrate that in environments characterized by a low (high) prevalence of fake news, the platform’s policy is more effective when the rewards it collects from content sharing are low relative to the penalties it incurs from the sharing of fake news (when the rewards it collects from content sharing are high in absolute terms).

Keywords: fake news; social learning; crowdsourcing; platform operations (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
https://doi.org/10.1287/mnsc.2019.3295 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:66:y:2020:i:5:p:1826-1846

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().