Dissecting a social bot powered by generative AI: anatomy, new trends and challenges

Abstract

The rise of social networks has transformed communication, information sharing and entertainment, but it has also facilitated the rise of harmful activities such as the spread of misinformation, often through the use of social bots. These automated accounts that mimic human behaviour have been implicated in significant events, including political interference and market manipulation. In this paper, we provide a comprehensive review of recent advances in social bot detection, with a particular focus on the role of generative AI and large language models. We present a new categorisation scheme for bots that aims to reduce class overlap while maintaining generality. In addition, we analyse the most commonly used datasets and state-of-the-art classification techniques, and through user profile-based measures, we use Explainable Artificial Intelligence (XAI) and data mining techniques to uncover factors that contribute to bot misclassification. Our findings contribute to the development of more robust detection methods, which are essential for mitigating the impact of malicious bots on online platforms.