Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909798643007488 |
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| author | Du, Cuiqianhe Chiang, Chia-En Huang, Tianyi Cui, Zikun |
| author_facet | Du, Cuiqianhe Chiang, Chia-En Huang, Tianyi Cui, Zikun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks Du, Cuiqianhe Chiang, Chia-En Huang, Tianyi Cui, Zikun Machine Learning 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. |
| title | Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.16936 |