Evidence map›Paper›PMID 39641349›Full record

ArticleJournal of interpersonal violence2025

Examining the Meaning of "Violence" Through Machine Learning Techniques.

Jolien van Breen, Emil Rijcken, Jaroslaw Kantorowicz, Marieke Liem

Abstract read
In one paragraph

Article in Journal of interpersonal violence, 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

4 authors.

Jolien van BreenLeiden University, Den Haag, Netherlands.ORCID 0000-0003-2776-7132
Emil RijckenEindhoven University of Technology, Netherlands.ORCID 0000-0003-3533-6500
Jaroslaw KantorowiczLeiden University, Den Haag, Netherlands.ORCID 0000-0002-1186-5427
Marieke LiemLeiden University, Den Haag, Netherlands.ORCID 0000-0002-2653-4356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper examines the meaning of violence in contemporary Western societies. Scholars have argued that in contemporary Western societies, the concept is expanding toward a broader understanding of violence, beyond its "traditional" usage in the context of crime and war. The current paper aims to generate empirical evidence that speaks to this question. We take the Netherlands as a case study and apply machine learning techniques to discourse on violence in 80,000 articles published in national newspapers between 2012 and 2021. Results show that the public discourse on violence in the Netherlands has a component that can be described as the familiar or "traditional" usage of the term violence-referring to violent political conflicts, or interpersonal violence such as child abuse. Beyond this, the term violence is associated with discourse on societal challenges. It appears in discourse on social media, political polarization, and social injustice faced by ethnic minorities, women, and the LGBT+ community. The later stages of the analysis demonstrated that the terms associated with social injustice in particular (e.g., "racist") become more closely associated with the concept of violence over time. In short, our findings support the notion that the collective understanding of violence in the Netherlands is developing toward a broader understanding of violence, beyond the context of crime and war. Specifically, in recent years the term violence is increasingly used in association with issues of social injustice.

Indexed as

Machine LearningSocial JusticeViolenceDatabases, FactualHumansNetherlandsNewspapers as Topicsocial injusticetopic modelingviolenceword embeddings

Identifiers

PMID39641349
PMCPMC12504789

What OpenQuestion holds

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LicenceCC BY-NC
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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.