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.

翻译：随着社交媒体平台的兴起，网络攻击行为日益增多，其涵盖范围广泛，包括网络欺凌、在线骚扰以及冒犯性与仇恨言论的传播。这些行为已引发显著的社会后果，从网络匿名性到现实世界的影响，如抑郁、自杀倾向乃至线下暴力事件。鉴于未受监管的攻击性内容所带来的社会风险，本文深入探讨攻击性内容检测与攻击性用户行为分析领域，旨在弥合现有研究间的隔阂。本文分析了相关定义的多样性，并提出了统一的网络攻击行为定义。我们系统梳理了攻击性内容检测的完整流程，涵盖数据集构建、特征选择与提取以及检测算法开发。进一步地，我们回顾了攻击行为分析的相关研究，这些研究探讨了网络攻击行为的影响因素、后果及其行为模式。本系统性文献综述是对网络攻击领域中内容检测与行为分析的交叉审视。整合性研究表明，将社会学洞见融入计算技术能有效预防网络攻击行为。最后，本文通过指出当前研究空白，鼓励在社会-计算融合的攻击行为分析统一领域开展进一步探索。