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

Andrea Zaccaria1, Michela Del Vicario2, Walter Quattrociocchi1,3

  • 1Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy.

Plos One
|January 29, 2019
PubMed
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We developed PopRank, an algorithm measuring Facebook page impact and user engagement through interactions. PopRank predicts future comments and posts, showing page impact is independent of user polarization.

Area of Science:

  • Social Network Analysis
  • Information Science
  • Computational Social Science

Background:

  • Social networks have transformed information access.
  • Understanding user-platform interactions is vital.
  • Existing metrics may not fully capture page influence and user engagement dynamics.

Purpose of the Study:

  • To introduce PopRank, an algorithm assessing Facebook page impact and user engagement.
  • To analyze the relationship between page popularity and user interaction patterns.
  • To determine factors influencing a page's impact, such as content quality and user polarization.

Main Methods:

  • Developed the PopRank algorithm based on user-platform interactions.
  • Analyzed mutual interactions between Facebook pages and users.

Related Experiment Videos

  • Evaluated PopRank's ability to predict page comments and future posts.

    • Main Results:

    • PopRank effectively measures Facebook page impact and user engagement.
    • High-impact pages attract low-engagement users, while high-engagement users interact with popular pages.
    • Page impact is minimally influenced by content quality and not by user polarization.

    Conclusions:

    • PopRank offers a novel method for evaluating social media influence.
    • The algorithm provides predictive insights into content performance.
    • User polarization does not significantly affect a page's perceived impact on social media.