MSM-BD: Multimodal Social Media Bot Detection Using Heterogeneous Information
Journal:
arXiv
Published Date:
Dec 31, 2024
Abstract
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.
