Social bot detection faces new challenges from AI chatbots
Social bot detection in the age of ChatGPT: Challenges and opportunities
Computers and SocietyComputation and Language
Summary
Social bots are automated accounts on platforms like Twitter that can spread misinformation or influence opinions. The rise of chatbots powered by advanced AI, like ChatGPT, makes detecting these bots harder because their conversations and behaviors look more human. The authors review current detection methods and point out where they fall short against such smart bots. They suggest new ideas, like using AI to create fake data for testing, detecting bots across different media and languages, and encouraging collaboration between platforms without exposing private user data.
What this means in practice
- •For social media platform teams: Improve detection tools by integrating multimodal and cross-platform analysis to better spot coordinated bot behavior enhanced by AI.
- •For privacy-focused security teams: Develop federated learning models that collaborate across organizations to detect bots while ensuring user data privacy is maintained.
A survey. It maps existing work.
Authors
Emilio Ferrara
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.