基于多模态信息不变和特定表示的社交机器人检测方法OA

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,named 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.