 Using Website Referrals to Identify Misinformation Rabbit HolesKevin T. Greene, Mayana Pereira, Nilima Pisharody, Rahul Dodhia, Juan Lavista Ferres and Jacob N Shapiro No x4dh7, OSF Preprints from Center for Open Science Abstract: Does the URL referral structure of websites lead users into "rabbit holes" of misinformation? Past work suggests algorithmic recommender systems on sites like YouTube lead users to view more misinformation. However, websites without algorithmic recommender systems have financial and political motivations to influence the movement of users, potentially creating browsing rabbit holes. We address this gap using browser telemetry that captures referrals to a large sample of domains rated as reliable or unreliable information sources. Our results suggest the incentives for unreliable sites to retain and monetize users create misinformation rabbit holes. After landing on an unreliable site, users are very likely to be referred to another page on the site. Further, unreliable sites are better at retaining users than reliable sites. We find less support for political motivations. While reliable and unreliable sites are largely disconnected from one another, the probability of traveling from one unreliable site to another is relatively low. Our findings indicate the need for additional focus on site-level incentives to shape traffic moving through their sites. Date: 2023-06-13 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:x4dh7 Access Statistics for this paper More papers in OSF Preprints from Center for Open Science |
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