MSM-BD: Multimodal Social Media Bot Detection Using Heterogeneous Information
This addresses the need for more advanced bot detection techniques to combat misuse on social media platforms, representing an incremental improvement in a domain-specific area.
The paper tackles the problem of detecting social bots on social media by proposing MSM-BD, a multimodal approach using heterogeneous information like images, texts, and user features, which achieves improved detection accuracy as validated on the TwiBot-22 dataset.
Although social bots can be engineered for constructive applications, their potential for misuse in manipulative schemes and malware distribution cannot be overlooked. This dichotomy underscores the critical need to detect social bots on social media platforms. Advances in artificial intelligence have improved the abilities of social bots, allowing them to generate content that is almost indistinguishable from human-created content. These advancements require the development of more advanced detection techniques to accurately identify these automated entities. Given the heterogeneous information landscape on social media, spanning images, texts, and user statistical features, we propose MSM-BD, a Multimodal Social Media Bot Detection approach using heterogeneous information. MSM-BD incorporates specialized encoders for heterogeneous information and introduces a cross-modal fusion technology, Cross-Modal Residual Cross-Attention (CMRCA), to enhance detection accuracy. We validate the effectiveness of our model through extensive experiments using the TwiBot-22 dataset.