Evidence map›Paper›PMID 38709721›Full record

ArticlePloS one2024

The hatred of all against all? Evidence from online community platforms in South Korea.

Jeong-Woo Koo, Chan S Suh, Jin Won Chung, Kyung-Ah Sohn, Kyungsik Han

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jeong-Woo KooDepartment of Sociology and the Social Innovation Convergence Program, Sungkyunkwan University, Seoul, Republic of Korea.
Chan S SuhDepartment of Sociology, Chung-Ang University, Seoul, Republic of Korea.
Jin Won ChungDepartment of Sociology, The Catholic University of Korea, Seoul, Republic of Korea.ORCID 0009-0003-8910-2619
Kyung-Ah SohnDepartment of Software and Computer Engineering, Ajou University, Suwon, Republic of Korea.
Kyungsik HanDepartment of Data Science, Hanyang University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOver several years of recent efforts to make sense and detect online hate speech, we still know relatively little about how hateful expressions enter online platforms and whether there are patterns and features characterizing the corpus of hateful speech.

objectiveIn this research, we introduce a new conceptual framework suitable for better capturing the overall scope and dynamics of the current forms of online hateful speech.

methodsWe adopt several Python-based crawlers to collect a comprehensive data set covering a variety of subjects from a multiplicity of online communities in South Korea. We apply the notions of marginalization and polarization in identifying patterns and dynamics of online hateful speech.

resultsOur analyses suggest that polarization driven by political orientation and age difference predominates in the hateful speech in most communities, while marginalization of social minority groups is also salient in other communities. Furthermore, we identify a temporal shift in the trends of online hate from gender to age based, reflecting the changing sociopolitical conditions within the polarization dynamics in South Korea.

conclusionBy expanding our understanding of how hatred shifts and evolves in online communities, our study provides theoretical and practical implications for both researchers and policy-makers.

Indexed as

InternetAdultFemaleHumansMaleMiddle AgedPoliticsRepublic of KoreaYoung Adult

Identifiers

PMID38709721
PMCPMC11073691

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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.