Evidence map›Paper›PMID 37379268›Full record

ArticlePloS one2023

Characterizing engagement dynamics across topics on Facebook.

Gabriele Etta, Emanuele Sangiorgio, Niccolò Di Marco, Michele Avalle, Antonio Scala, Matteo Cinelli, Walter Quattrociocchi

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  5. Patterns of linguistic simplification on social media platforms over time.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  6. Article
  7. 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

7 authors.

Gabriele EttaCenter of Data Science and Complexity for Society, Department of Computer Science, Sapienza Università di Roma, Roma, Italy.ORCID 0000-0001-5603-0100
Emanuele SangiorgioDepartment of Social Sciences and Economics, Sapienza Università di Roma, Roma, Italy.
Niccolò Di MarcoDepartment of Mathematics and Computer Science, University of Florence, Firenze FI, Italy.ORCID 0000-0003-4335-7328
Michele AvalleCenter of Data Science and Complexity for Society, Department of Computer Science, Sapienza Università di Roma, Roma, Italy.
Antonio ScalaISC-CNR UoS Sapienza, Rome, Italy.
Matteo CinelliCenter of Data Science and Complexity for Society, Department of Computer Science, Sapienza Università di Roma, Roma, Italy.ORCID 0000-0003-3899-4592
Walter QuattrociocchiCenter of Data Science and Complexity for Society, Department of Computer Science, Sapienza Università di Roma, Roma, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social media platforms heavily changed how users consume and digest information and, thus, how the popularity of topics evolves. In this paper, we explore the interplay between the virality of controversial topics and how they may trigger heated discussions and eventually increase users' polarization. We perform a quantitative analysis on Facebook by collecting ∼57M posts from ∼2M pages and groups between 2018 and 2022, focusing on engaging topics involving scandals, tragedies, and social and political issues. Using logistic functions, we quantitatively assess the evolution of these topics finding similar patterns in their engagement dynamics. Finally, we show that initial burstiness may predict the rise of users' future adverse reactions regardless of the discussed topic.

Indexed as

Social MediaSocial NetworkingHumans

Identifiers

PMID37379268
PMCPMC10306180

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.