Enhancing misinformation countermeasures: a multimodal approach to twitter bot detection

Abstract Identifying bots on X (formerly Twitter) is essential for preventing misinformation and ensuring user safety. However, current models face several challenges: (i) the use of outdated techniques and attributes, particularly those trained on older datasets; (ii) reproducibility problems stemming from unique and insufficiently detailed methodologies; (iii) shallow analysis depth, with few studies investigating all combinations of feature-based, text-based, and graph-based methods; (iv) a lack of thorough literature reviews to pinpoint effective characteristics for future research. To overcome these gaps, this study proposes a novel multimodal bot detection framework which integrates user profile features, text analysis, and graph-based techniques. Utilizing the recent TwiBot-22 dataset, the model combines semantic text information from user profiles and tweets with graph representations of user interactions, including novel relationships like list ownership and all applicable user profile features, encompassing both those previously used in state-of-the-art models and newly introduced features designed to enhance detection accuracy. The study compares the proposed model against existing approaches and demonstrates a significant improvement in detection accuracy, exceeding the best-performing models by 5.48%. The research contributes a comprehensive evaluation of feature sets, highlights the advantages of incorporating multiple detection strategies, and identifies key parameters for optimizing bot detection models.