Social bot detection in the age of ChatGPT: Challenges and opportunities
Open accessAbstract
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.
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LLMs have the potential to enhance the capabilities of social bots.
“LLMs also have the potential to enhance the capabilities of social bots, akin to previously mentioned cyber-social threats (Grimme et al., 2022;Ferrara, 2023).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Ferrara, 2023
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- Authors
- Ferrara, E.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Ferrara, Emilio
- ORCID
- Ferrara, Emilio
- Venue
- First Monday
- Publisher
- University of Illinois Libraries
- Published
- 2023-06-15
- ISSN
- 1396-0458 (ISSN-L)
- 1396-0466
- OpenAlex
- W4380887356
- Cited by
- 84
- References
- 10
- Citations by year
- 2026: 35 · 2025: 22 · 2024: 21 · 2023: 6
- FWCI
- 19.82
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- First Monday (publishedVersion, cc-by-nc-sa)
- SSRN Electronic Journal (acceptedVersion)
- First Monday (acceptedVersion, cc-by-nc-sa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- social bot detection, ChatGPT, generative AI agents, synthetic data generation, multimodal detection, low-resource languages, federated learning, user privacy
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