Evidence map›Paper›PMID 39816737›Full record

ArticleRoyal Society open science2025

Finding polarized communities and tracking information diffusion on Twitter: a network approach on the Irish Abortion Referendum.

Caroline B Pena, Pádraig MacCarron, David J P O'Sullivan

Abstract read
In one paragraph

Article in Royal Society open science, 2025. 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

3 authors.

Caroline B PenaMathematics Application Consortium for Science and Industry (MACSI), University of Limerick, Limerick, Ireland.ORCID https://orcid.org/0009-0007-2329-8638
Pádraig MacCarronMathematics Application Consortium for Science and Industry (MACSI), University of Limerick, Limerick, Ireland.
David J P O'SullivanMathematics Application Consortium for Science and Industry (MACSI), University of Limerick, Limerick, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The analysis of social networks enables the understanding of social interactions, polarization of ideas and the spread of information, and therefore plays an important role in society. We use Twitter data-as it is a popular venue for the expression of opinion and dissemination of information-to identify opposing sides of a debate and, importantly, to observe how information spreads between these groups in our current polarized climate. To achieve this, we collected over 688 000 tweets from the Irish Abortion Referendum of 2018 to build a conversation network from users' mentions with sentiment-based homophily. From this network, community detection methods allow us to isolate yes- or no-aligned supporters with high accuracy (90.9%). We supplement this by tracking how information cascades spread via over 31 000 retweet cascades. We found that very little information spread between polarized communities. This provides a valuable methodology for extracting and studying information diffusion on large networks by isolating ideologically polarized groups and exploring the propagation of information within and between these groups.

Indexed as

community detectionInformation diffusionpolarizationsocial networktext analysisTwitter

Identifiers

PMID39816737
PMCPMC11732405

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.