TY - UNPB
T1 - Themes of Revenge
T2 - Automatic Identification of Vengeful Content in Textual Data
AU - Neuman, Yair
AU - Erez, Eden Shalom
AU - Tschantret, Joshua
AU - Weiss, Hayden
PY - 2022/5/3
Y1 - 2022/5/3
N2 - 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.
AB - 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.
KW - cs.CL
KW - J.4
U2 - 10.48550/arXiv.2205.01731
DO - 10.48550/arXiv.2205.01731
M3 - Preprint
BT - Themes of Revenge
ER -