Social bot detection method based on multimodal information invariant and specific representation

Social bots have continuously evolved during their development, posing significant challenges to existing detection models. To address this, we propose a novel social bot detection framework, BotSAI. This framework first employs customized encoders to extract multi-dimensional feature representations from user metadata, text, and heterogeneous social network graphs. Specifically, the graph encoder achieves efficient and balanced aggregation of neighborhood information through oversampling and a local relation transformer. Subsequently, a multi-channel representor maps user representations into invariant subspaces and specific subspaces to enhance their features. Finally, the enhanced user representations are integrated and refined using a self-attention mechanism. Experimental results demonstrate that BotSAI outperforms state-of-the-art methods on two authoritative social bot detection benchmarks. Furthermore, systematic experiments reveal the impact of different social relationships on detection accuracy, providing new research perspectives for social bot detection.