Algorithmic inference, political interest, and exposure to news and politics on Facebook

Abstract

ABSTRACT The visibility of news and politics in a Facebook newsfeed depends on the actions of a diverse set of actors: users, their friends, content publishers such as news organizations, advertisers, and algorithms. The focus of this paper is on untangling the role of this last actor from the others. We ask, how does Facebook algorithmically infer what users are interested in, and how do interest inferences shape news exposure? We weave together survey data and interest categorization data from participants’ Facebook accounts to audit the algorithmic interest classification system on Facebook. These data allow us to model the role of algorithmic inference in shaping content exposure. We show that algorithmic ‘sorting out’ of users has consequences for who is exposed to news and politics on Facebook. People who are algorithmically categorized as interested in news or politics are more likely to attract this kind of content into their feeds – above and beyond their self-reported interest in civic content.