Evidence map›Paper›PMID 41195086›Full record

ArticleProceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media2025

Unifying the Extremes: Developing a Unified Model for Detecting and Predicting Extremist Traits and Radicalization.

Allison Lahnala, Vasudha Varadarajan, Lucie Flek, H Andrew Schwartz, Ryan L Boyd

Abstract read
In one paragraph

Article in Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media, 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

5 authors.

Allison LahnalaDepartment of Computing & Software, McMaster University.
Vasudha VaradarajanDepartment of Computer Science, Stony Brook University.
Lucie FlekBonn-Aachen International Center for Information Technology, University of Bonn.
H Andrew SchwartzDepartment of Computer Science, Stony Brook University.
Ryan L BoydDepartment of Psychology, University of Texas at Dallas.

Funding

Large-scale Data Scientific Assessment of Unhealthy Alcohol Consumption Among Front-Line Restaurant WorkersR01AA028032 · NIAAA · STATE UNIVERSITY NEW YORK STONY BROOK · PI SCHWARTZ, HANSEN ANDREW · 2020 to 2023
$2.5M
NIAAA NIH HHS R01 AA028032
6 · The paper itself

Abstract

The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper, we propose a novel method for extracting and analyzing extremist discourse across a range of online ideological community forums. By focusing on verbal behavioral signatures of extremist traits, we develop a framework for quantifying extremism at both user and community levels. Our research identifies 11 distinct factors, which we term "The Extremist Eleven," as a generalized psychosocial model of extremism. Applying our method to various online communities, we demonstrate an ability to characterize ideologically diverse communities across the 11 extremist traits. We demonstrate the power of this method by analyzing user histories from members of the incel community. We find that our framework accurately predicts which users join the incel community up to 10 months before their actual entry with an AUC of > 0.6, steadily increasing to AUC ~ 0.9 three to four months before the event. Further, we find that upon entry into an ideological forum, the users tend to maintain their level of extremist traits within the community, while still remaining distinguishable from the general online discourse. Our findings contribute to the study of extremism by introducing a more holistic, cross-ideological approach that transcends traditional, trait-specific models.

Identifiers

PMID41195086
PMCPMC12584583

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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.