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.

翻译：尽管社交机器人可被设计用于建设性应用，但其在操纵性方案和恶意软件传播中的滥用潜力不容忽视。这种二元性凸显了在社交媒体平台上检测社交机器人的关键需求。人工智能的进步提升了社交机器人的能力，使其能够生成几乎与人类创作内容无异的文本。这些进展要求开发更先进的检测技术来准确识别这些自动化实体。鉴于社交媒体上涵盖图像、文本及用户统计特征的异质信息景观，我们提出MSM-BD——一种利用异质信息的多模态社交媒体机器人检测方法。MSM-BD集成了面向异质信息的专用编码器，并引入跨模态融合技术——跨模态残差交叉注意力（CMRCA）以提升检测精度。我们通过使用TwiBot-22数据集进行的广泛实验验证了模型的有效性。