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

arXiv:2610.02386v1 Announce Type: new 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.

Item discusses challenges and opportunities in social bot detection.

This is a research survey identifying challenges and proposing directions, not reporting a concrete deployed harm or breakthrough. It implies ongoing erosion of ability to detect AI-driven manipulation online but also outlines constructive research paths, netting a mildly negative but mostly balanced implication.

The paper identifies increasing challenges in detecting social bots due to sophisticated AI. This implies a negative societal impact as detection becomes harder, potentially increasing disinformation and manipulation. However, it also proposes solutions, indicating a balanced but leaning negative outlook.

The text discusses technical challenges and opportunities in social bot detection. It does not make substantive claims about AI's effect on society.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   6ec50459-457e-4128-96ba-e2700929e9ab
           https://arxiv.org/abs/2610.02386
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EVIDENCE   2 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -26  (Adverse)
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CONFIDENCE 70%