DMIBot: Dynamic Multimodal Interaction for Twitter Bot Detection

Abstract

Social bot detection aims to precisely identify and mitigate the effects of bots spreading misinformation and manipulating public opinion by analyzing users’ textual content and network relationships on social media. Many existing methods rely on graph-based modalities, and while some incorporate multiple modalities, they often struggle to effectively capture the intricate interrelations within multimodal data. To address this limitation, we propose DMIBot, a dynamic multimodal interaction framework that comprehensively integrates metadata, text, and graph modalities. The framework utilizes a relational-aware graph aggregation module to enhance user node representations and a dynamic mixture of experts module to selectively engage the most appropriate expert for each sample. Furthermore, we introduce a synergistic interaction module that employs layer-wise recursive deep interaction mechanisms for efficient and effective integration of the three modalities. Experiments demonstrate that DMIBot outperforms nine benchmark models on two public datasets, achieving state-of-the-art performance in bot detection. Additional evaluations confirm its capabilities in ensuring the framework’s effectiveness and robustness in detecting Twitter bots.