# Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks

arXiv: https://arxiv.org/abs/2509.16936 · HTML: https://mlsanity.com/paper/2509.16936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [A General Black-box Adversarial Attack on Graph-based Fake News Detectors](https://mlsanity.com/paper/2404.15744/) (2024)
- [Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation](https://mlsanity.com/paper/2007.02164/) (2020)
- [Fake News Detection Through Graph-based Neural Networks: A Survey](https://mlsanity.com/paper/2307.12639/) (2023)

## Abstract (arXiv)

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.