Themes of Revenge: Automatic Identification of Vengeful Content in Textual Data
This work addresses the screening of solo perpetrators for security applications, but it is incremental as it applies existing methods to new data.
The researchers tackled the problem of automatically identifying vengeful themes in textual data, achieving promising results across four datasets including social media and terrorist texts, even with imbalanced data.
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.