  The rise of social media platforms has led to an increase in cyber-aggressive
behavior, encompassing a broad spectrum of hostile behavior, including
cyberbullying, online harassment, and the dissemination of offensive and hate
speech. These behaviors have been associated with significant societal
consequences, ranging from online anonymity to real-world outcomes such as
depression, suicidal tendencies, and, in some instances, offline violence.
Recognizing the societal risks associated with unchecked aggressive content,
this paper delves into the field of Aggression Content Detection and Behavioral
Analysis of Aggressive Users, aiming to bridge the gap between disparate
studies. In this paper, we analyzed the diversity of definitions and proposed a
unified cyber-aggression definition. We examine the comprehensive process of
Aggression Content Detection, spanning from dataset creation, feature selection
and extraction, and detection algorithm development. Further, we review studies
on Behavioral Analysis of Aggression that explore the influencing factors,
consequences, and patterns associated with cyber-aggressive behavior. This
systematic literature review is a cross-examination of content detection and
behavioral analysis in the realm of cyber-aggression. The integrated
investigation reveals the effectiveness of incorporating sociological insights
into computational techniques for preventing cyber-aggressive behavior.
Finally, the paper concludes by identifying research gaps and encouraging
further progress in the unified domain of socio-computational aggressive
behavior analysis.
