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Area of Science:

  • Computational Social Science
  • Psychology
  • Sociology

Background:

  • Online ideological movements increasingly form extremist factions.
  • Existing research on radicalization is often ideology-specific, lacking generalizable characterization.
  • Quantifying extremism across diverse online communities remains a challenge.

Purpose of the Study:

  • To propose a novel, generalizable method for extracting and analyzing extremist discourse.
  • To develop a framework for quantifying extremism at user and community levels.
  • To identify a generalized psychosocial model of extremism.

Main Methods:

  • Extraction and analysis of extremist discourse from online ideological communities.
  • Focus on verbal behavioral signatures of extremist traits.
  • Development of a framework quantifying extremism based on 11 distinct factors ('The Extremist Eleven').

Main Results:

  • Successfully characterized ideologically diverse online communities across the 11 extremist traits.
  • Predicted user entry into the incel community up to 10 months prior with high accuracy (AUC > 0.6, increasing to ~0.9).
  • Observed that users maintain extremist traits upon joining forums, distinct from general online discourse.

Conclusions:

  • Introduced a holistic, cross-ideological approach to studying extremism.
  • The 'Extremist Eleven' framework offers a generalized psychosocial model for quantifying online extremism.
  • The method demonstrates predictive power for identifying individuals at risk of radicalization.