Evidence map›Paper›PMID 30689652›Full record

ArticlePloS one2019

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

Andrea Zaccaria, Michela Del Vicario, Walter Quattrociocchi, Antonio Scala, Luciano Pietronero

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Andrea ZaccariaIstituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy.ORCID 0000-0002-4478-3292
Michela Del VicarioIMT School for Advanced Studies, Lucca, Italy.
Walter QuattrociocchiIstituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy.
Antonio ScalaIstituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy.
Luciano PietroneroIstituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of social networks revolutionized the way people access to information sources. Understanding the complex relationship between these sources and users is crucial. 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 future posts. Pages' impact turns out to be slightly dependent on the quality of pages' informative content (e.g., science vs conspiracy) but independent of users' polarization.

Indexed as

AlgorithmsOnline Social NetworkingSocial MediaHumans

Identifiers

PMID30689652
PMCPMC6349323

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.