Themes of Revenge: Automatic Identification of Vengeful Content in Textual Data

Yair Neuman, Eden Shalom Erez, Joshua Tschantret, Hayden Weiss

Research output: Working paper/PreprintPreprint

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Abstract

Revenge is a powerful motivating force reported to underlie the behavior of various solo perpetrators, from school shooters to right wing terrorists. In this paper, we develop an automated methodology for identifying vengeful themes in textual data. Testing the model on four datasets (vengeful texts from social media, school shooters, Right Wing terrorist and Islamic terrorists), we present promising results, even when the methodology is tested on extremely imbalanced datasets. The paper not only presents a simple and powerful methodology that may be used for the screening of solo perpetrators but also validate the simple theoretical model of revenge.
Original languageEnglish
Pages1-21
StatePublished - 3 May 2022

Keywords

  • cs.CL
  • J.4

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