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.

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• Character-level Convolutional Networks for Text Classification
• MesoNet: a Compact Facial Video Forgery Detection Network
• Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign

Cited by in corpus (4)

• GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models
• Anatomy of an AI-powered malicious social botnet
• Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls
• Multimodal Detection of Bots on X (Twitter) using Transformers