基于多模态信息不变和特定表示的社交机器人检测方法

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

摘要

社交机器人在发展过程中不断进化，给现有检测模型带来挑战。为此，提出了一种新的社交机器人检测框架 BotSAI。该框架首先采用定制化编码器从用户元数据、文本和社交网络异构图中提取多维度特征表示。其中，图编码器通过过采样和局部关系转换器实现邻居信息的高效均衡聚合。随后，利用多通道表示器将用户表示映射至不变子空间和特定子空间以增强其特征。最后，通过自注意力机制对增强后的用户表示进行整合与提炼。实验结果表明，BotSAI 在两大权威社交机器人检测基准上均超越了现有方法。此外，系统性实验揭示了不同社交关系对检测精度的影响，为社交机器人检测提供了新的研究视角。

Abstract

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.

关键词

社交机器人检测 / 社交网络异构图 / 不变与特定子空间 / 自注意力机制

Key words

social bot detection / heterogeneous graph of social networks / invariant and specific subspaces / self-attention mechanism

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基金资助

河北省创新能力提升计划资助项目(22567626H)

中央引导地方科技发展资金项目(246Z0306G)