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.
Predicting the Type and Target of Offensive Posts in Social Media
University of Wolverhampton（伍尔弗汉普顿大学）；Harvard Medical School（哈佛医学院）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；IBM Research（IBM研究院）；Columbia University（哥伦比亚大学）；Bhim Rao Ambedkar University（比姆·拉奥·安贝德卡尔大学）
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse
Panlingua Language Processing LLP（泛语语言处理有限责任合伙公司）；Dr Bhimrao Ambedkar University（比姆拉奥·安贝德卡博士大学）；Indian Institute of Technology-Kharagpur（印度理工学院卡拉格普尔分校）
Cyberbullying Detection via Aggression-Enhanced Prompting
NICE Research Group & Institute for People-Centred AI, School of Computer Science & Electronic Engineering, University of Surrey, UK（NICE研究组及以人为本的人工智能研究所、计算机科学与电子工程学院、萨里大学）
Developing a Multilingual Annotated Corpus of Misogyny and Aggression
Jawaharlal Nehru University（贾瓦哈拉尔·尼赫鲁大学）；Dr. Bhimrao Ambedkar University（比姆拉奥·安贝德卡尔博士大学）；Panlingua Language Processing LLP（潘林格语言处理有限责任合伙公司）；Indian Institute of Technology（印度理工学院）
Multimodal Social Media Analysis for Gang Violence Prevention
DFKI（德国人工智能研究中心）；TU Kaiserslautern（凯泽斯劳滕工业大学）；Columbia University（哥伦比亚大学）；University of Turin（都灵大学）
帮派暴力是美国主要城市的一个严重问题，最近的研究[Patton et al. 2017]发现了社交媒体传播的证据，这些证据可能与帮派活动高发社区的暴力行为有关。在本文中，我们与计算机科学家和社会工作研究人员合作，他们在帮派暴力方面具有专业知识，分析如何利用在Twitter上提到帮派协会的年轻人发布的公共推文来自动检测心理社会因素和条件，这些因素和条件可能有助于社会工作者和暴力外展工作者进行预防和早期干预计划。为此，我们开发了一种严格的方法来收集和注释推文。我们收集了1,851条推文和相关的注释，这些注释与视觉概念和社会心理代码有关：攻击、损失和物质使用。这些守则与社会工作干预有关，因为它们代表了社交媒体上暴力的可能途径。我们比较了各种方法分类推文到这三类，只使用文本的推文，只有图像的推文，或两种形式作为输入到分类器。特别是，我们分析了中级视觉概念的有用性和不同模态的作用，这个推文分类任务。我们的实验表明，单独的，文本信息占主导地位的损失类的分类性能，而图像信息占主导地位的侵略和物质使用类。我们的多模态方法提供了一个非常有前途的改善（18%相对平均精度）超过最好的单一模态方法。最后，我们还说明了理解社交媒体数据的复杂性，并阐述了开放的挑战。
Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院人工智能安全国家重点实验室，计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière（索邦大学，巴黎脑研究所-ICM，法国国家科学研究中心，法国国家信息与自动化研究所，法国国家健康与医学研究院，巴黎公共医疗集团，皮蒂-萨尔皮特里埃医院）
The cognitive, affective, and behavioral expression of self-stigma among people who use drugs in online substance use communities
Department of Information Science, Drexel University（德雷塞尔大学信息科学系）；Department of Human Development and Family Sciences, University of Delaware（特拉华大学人类发展与家庭科学系）
LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans
Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研究与开发研究所）；University of Belgrade, Institute for Philosophy and Social Theory, Digital Society Lab（贝尔格莱德大学，哲学与社会理论研究所，数字社会实验室）；Complexity Science Hub, Vienna, Austria（维也纳复杂科学中心）；Graz University of Technology, Graz, Austria（技术大学格拉茨，格拉茨，奥地利）；University of Novi Sad, Faculty of Philosophy, Novi Sad, Serbia（诺维萨德大学，哲学学院，诺维萨德，塞尔维亚）；University of Trier, Trier, Germany（特里尔大学，特里尔，德国）；Vrije University Amsterdam, Amsterdam, Netherlands（阿姆斯特丹自由大学，阿姆斯特丹，荷兰）
