Social bot detection in the age of ChatGPT: Challenges and opportunities

Abstract:We present a comprehensive overview of the challenges and opportunities in social bot detection in the context of the rise of sophisticated AI-based chatbots. By examining the state of the art in social bot detection techniques and the more salient real-world application to date, we identify gaps and emerging trends in the field, with a focus on addressing the unique challenges posed by AI-generated conversations and behaviors. We suggest potentially promising opportunities and research directions in social bot detection, including (i) the use of generative agents for synthetic data generation, testing and evaluation; (ii) the need for multimodal and cross-platform detection based on network and behavioral signatures of coordination and influence; (iii) the opportunity to extend bot detection to non-English and low-resource language settings; and, (iv) the room for development of collaborative, federated learning detection models that can help facilitate cooperation between different organizations and platforms while preserving user privacy.

Computers and Society (cs.CY); Computation and Language (cs.CL)

arXiv:2610.02386 [cs.CY]

(or arXiv:2610.02386v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2610.02386 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.5210/fm.v28i6.13185 Focus to learn more DOI(s) linking to related resources

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