{"title":"网络攻击调查:社交媒体平台上的内容检测和行为分析","authors":"Swapnil Mane, Suman Kundu, Rajesh Sharma","doi":"10.1145/3711125","DOIUrl":null,"url":null,"abstract":"The proliferation of social media has increased cyber-aggressive behavior behind the freedom of speech, posing societal risks from online anonymity to real-world consequences. This article systematically reviews Aggression Content Detection and Behavioral Analysis to address these risks. Content detection is vital for handling explicit aggression, and behavior analysis offers insights into underlying dynamics. The paper analyzes diverse definitions, proposes a unified cyber-aggression definition, and reviews the process of Aggression Content Detection, emphasizing dataset creation, feature extraction, and algorithm development. Additionally, examines Behavioral Analysis studies that explore influencing factors, consequences, and patterns of online aggression. We cross-examine content detection and behavioral analysis, revealing the effectiveness of integrating sociological insights into computational techniques for preventing cyber-aggression. We conclude by identifying research gaps that urge progress in the integrative domain of socio-computational aggressive behavior analysis.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"45 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Survey on Online Aggression: Content Detection and Behavioural Analysis on Social Media Platforms\",\"authors\":\"Swapnil Mane, Suman Kundu, Rajesh Sharma\",\"doi\":\"10.1145/3711125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proliferation of social media has increased cyber-aggressive behavior behind the freedom of speech, posing societal risks from online anonymity to real-world consequences. This article systematically reviews Aggression Content Detection and Behavioral Analysis to address these risks. Content detection is vital for handling explicit aggression, and behavior analysis offers insights into underlying dynamics. The paper analyzes diverse definitions, proposes a unified cyber-aggression definition, and reviews the process of Aggression Content Detection, emphasizing dataset creation, feature extraction, and algorithm development. Additionally, examines Behavioral Analysis studies that explore influencing factors, consequences, and patterns of online aggression. We cross-examine content detection and behavioral analysis, revealing the effectiveness of integrating sociological insights into computational techniques for preventing cyber-aggression. We conclude by identifying research gaps that urge progress in the integrative domain of socio-computational aggressive behavior analysis.\",\"PeriodicalId\":50926,\"journal\":{\"name\":\"ACM Computing Surveys\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":23.8000,\"publicationDate\":\"2025-01-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Computing Surveys\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3711125\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3711125","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
A Survey on Online Aggression: Content Detection and Behavioural Analysis on Social Media Platforms
The proliferation of social media has increased cyber-aggressive behavior behind the freedom of speech, posing societal risks from online anonymity to real-world consequences. This article systematically reviews Aggression Content Detection and Behavioral Analysis to address these risks. Content detection is vital for handling explicit aggression, and behavior analysis offers insights into underlying dynamics. The paper analyzes diverse definitions, proposes a unified cyber-aggression definition, and reviews the process of Aggression Content Detection, emphasizing dataset creation, feature extraction, and algorithm development. Additionally, examines Behavioral Analysis studies that explore influencing factors, consequences, and patterns of online aggression. We cross-examine content detection and behavioral analysis, revealing the effectiveness of integrating sociological insights into computational techniques for preventing cyber-aggression. We conclude by identifying research gaps that urge progress in the integrative domain of socio-computational aggressive behavior analysis.
期刊介绍:
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.