PopRank: Ranking pages' impact and users' engagement on Facebook

Abstract

Users online tend to acquire information adhering to their system of beliefs and to ignore dissenting information. Such dynamics might affect page popularity. In this paper we introduce an algorithm, that we call PopRank, to assess both the Impact of Facebook pages as well as users' Engagement on the basis of their mutual interactions. The ideas behind the PopRank are that i) high impact pages attract many users with a low engagement, which means that they receive comments from users that rarely comment, and ii) high engagement users interact with high impact pages, that is they mostly comment pages with a high popularity. The resulting ranking of pages can predict the number of comments a page will receive and the number of its posts. Pages impact turns out to be slightly dependent on pages' informative content (e.g., science vs conspiracy) but independent of users' polarization.

arXiv comment10 pages, 5 figures

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Cited by in corpus (4)

• Nestedness in complex networks: Observation, emergence, and implications
• Selective Exposure shapes the Facebook News Diet
• Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms
• Drifts and Shifts: Characterizing the Evolution of Users Interests on Reddit