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

TL;DR

PopRank is an algorithm designed to evaluate Facebook pages' impact and users' engagement by analyzing mutual interactions, revealing that impact correlates with low user engagement and high-impact users tend to interact with popular pages.

Contribution

The paper introduces PopRank, a novel algorithm that jointly assesses page impact and user engagement based on interaction patterns, providing insights into influence dynamics on Facebook.

Findings

High impact pages attract many low-engagement users.

High engagement users tend to interact with high-impact pages.

Page impact is slightly influenced by content type but not by user polarization.

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.