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
Summary
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.
- 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.
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