发表机构
National School of Development, Peking University; College of Information and Electrical Engineering, China Agricultural University; School of Foreign Studies, Beijing Language and Culture University; Artificial Intelligence Research Institute, Tsinghua University（北京大学国家发展学院; 中国农业大学信息与电气工程学院; 北京语言大学外国语学院; 清华大学人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对直播平台协同异常行为早期检测难题，提出多模态框架MM-FGDNet，经实验验证其检测性能优于基线模型，可实现有效且可扩展的主动检测。
AI 中文摘要
随着直播电商和数字营销的快速发展，异常营销行为变得愈发隐蔽且在异质模态间协同发生，对平台治理和早期风险识别构成挑战。本文提出MM-FGDNet，一种数据驱动的多模态框架，用于从互补的时间演化和群组结构视角检测大规模直播环境中的异常行为。跨模态时间对齐模块将视频、文本、音频和用户行为映射到统一的时间语义空间；时间欺诈模式模块捕捉从微弱早期信号到突发爆发的演变过程；协同操纵模块识别有组织用户群组与自动化账户间的协同交互。在真实多平台直播电商数据集上的实验表明，MM-FGDNet优于代表性基线，实现了0.927的AUC、0.847的F1值、0.861的精确率、0.834的召回率及0.689的早期检测分数，同时降低了误报率。 ablation研究验证了各模块的贡献，跨域实验证明其对新主播、产品类别和平台具有稳定的泛化能力。这些结果表明，MM-FGDNet为直播系统中协同异常行为的主动检测提供了有效且可扩展的解决方案。
英文摘要
With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed and coordinated across heterogeneous modalities, challenging platform governance and early risk identification. We propose MM-FGDNet, a data-driven multimodal framework for detecting abnormal behavior in large-scale live-streaming environments from complementary temporal-evolution and group-structure perspectives. A cross-modal temporal alignment module maps video, text, audio, and user behavior into a unified temporal semantic space. A temporal fraud-pattern module captures the progression from weak early signals to abrupt outbreaks, while a cooperative manipulation module identifies coordinated interactions among organized user groups and automated accounts. Experiments on real-world multi-platform live-streaming e-commerce datasets show that MM-FGDNet outperforms representative baselines, achieving an AUC of 0.927, F1 of 0.847, precision of 0.861, recall of 0.834, and an Early Detection Score of 0.689, while reducing false alarms. Ablation studies validate the contribution of each module, and cross-domain experiments demonstrate stable generalization to new streamers, product categories, and platforms. These results indicate that MM-FGDNet provides an effective and scalable solution for proactive detection of coordinated abnormal behavior in live-streaming systems.
Comments27 pages, 6 figures
Journal refElectronics 15(4) (2026) 769
DOI：10.3390/electronics15040769
