IMMENSE：社交网络中的归纳式多视角用户分类
IMMENSE: Inductive Multi-perspective User Classification in Social Networks
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中文总结 AI 辅助
本研究提出基于机器学习的IMMENSE方法，整合内容语义、社交关系、空间信息的多视角混合分类策略，采用归纳式学习，在Twitter/X数据集上优于五种最先进方法，可用于检测社交网络恶意用户。
中文摘要 AI 辅助
在线社交网络越来越多地向人们推送传播歧视性、仇恨及暴力内容的用户，年轻用户尤其易受此类内容影响，可能产生有害的心理与社会后果。鉴于当前社交网络在发布内容量和用户数量上的庞大规模，迫切需要有效系统协助执法机构（LEAs）识别和处理传播恶意内容的用户。本研究提出IMMENSE这一基于机器学习的恶意社交网络用户检测方法，采用混合分类策略，整合三类视角：用户发布内容的语义、用户间的社交关系以及空间信息，这类上下文视角有望提升仅基于文本分析的分类性能。重要的是，IMMENSE采用归纳式学习方法，无需耗时且成本高昂的模型重训练流程即可对先前未见过的用户或全新网络进行分类。在真实Twitter/X数据集上开展的实验显示，IMMENSE优于五个当前最先进的对比方法，证实了其混合方法在社交网络监控系统中有效部署的优势。
英文摘要
Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content. Young users, in particular, are vulnerable to exposure to such content, which can have harmful psychological and social repercussions. Given the massive scale of today's social networks, in terms of both published content and number of users, there is an urgent need for effective systems to aid Law Enforcement Agencies (LEAs) in identifying and addressing users that disseminate malicious content. In this work we introduce IMMENSE, a machine learning-based method for detecting malicious social network users. Our approach adopts a hybrid classification strategy that integrates three perspectives: the semantics of the users' published content, their social relationships and their spatial information. Such contextual perspectives potentially enhance classification performance beyond text-only analysis. Importantly, IMMENSE employs an inductive learning approach, enabling it to classify previously unseen users or entire new networks without the need for costly and time-consuming model retraining procedures. Experiments carried out on a real-world Twitter/X dataset showed the superiority of IMMENSE against five state of the art competitors, confirming the benefits of its hybrid approach for effective deployment in social network monitoring systems.
发表机构
- University of Bari（巴里大学）
- University of Pisa（比萨大学）
- National Interuniversity Consortium for Informatics (CINI)（全国校际信息学联盟（CINI））
- Jožef Stefan Institute（约瑟夫·斯特凡研究所）
机构由 AI 辅助整理，请以论文原文为准。
