融合文本与关系图谱，精准识别社交网络高风险用户。
Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks
- 结合NLP与异构图神经网络，挖掘文本语义与用户关系结构。
- 在多平台真实数据上显著优于单一模态方法。
- 适合社交安全监控、舆情分析等应用领域。
本文提出一种创新的多模态方法，用于检测社交媒体用户潜在危险倾向。通过自然语言处理（NLP）对用户生成文本进行语义分析、情感识别和关键词提取，捕捉细微风险信号。同时，基于社交互动构建异构用户关系图，设计新型关系图卷积网络，建模用户关系、注意力关系与内容传播路径，挖掘关键结构信息与行为模式。最终将文本特征与图结构信息融合，提升风险用户发现能力。在多个平台的真实社交数据集上实验表明，该模型显著优于单模态方法。
原文摘要 · Abstract (English)
This paper focuses on the detection of potentially dangerous tendencies of social media users in an innovative multimodal way. We integrate Natural Language Processing (NLP) and Graph Neural Networks (GNNs) together. Firstly, we apply NLP on the user-generated text and conduct semantic analysis, sentiment recognition and keyword extraction to get subtle risk signals from social media posts. Meanwhile, we build a heterogeneous user relationship graph based on social interaction and propose a novel relational graph convolutional network to model user relationship, attention relationship and content dissemination path to discover some important structural information and user behaviors. Finally, we combine textual features extracted from these two models above with graph structural information, which provides a more robust and effective way to discover at-risk users. Our experiments on real social media datasets from different platforms show that our model can achieve significant improvement over single-modality methods.
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