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

TL;DR

This paper introduces an automated method to detect vengeful themes in text, aiding in identifying potential perpetrators and validating a theoretical model of revenge across diverse datasets.

Contribution

It presents a novel, effective methodology for identifying revenge-related content in textual data, applicable to various real-world contexts and datasets.

Findings

Promising detection results on social media and terrorist texts

Effective even with highly imbalanced datasets

Supports validation of a simple revenge theory

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.