COMPARATIVE ANALYSIS ON DEEP LEARNING METHODS FOR BOT DETECTION

Social bots are automated accounts on social media that are managed by software and overseen by people behind the scenes. While some bots are created for positive reasons, like sharing news updates or offering assistance in crises, others have been misused to spread false information, rumors, or influence political events. There are tools in place to identify and eliminate harmful bots automatically, but creators are continually evolving their techniques to evade detection. Hence, there is a pressing need for improved ways to differentiate between genuine and automated bot accounts. In recent years, several research studies have delved into the realm of social media bot detection, offering an extensive overview of different detection strategies, including advanced approaches such as machine learning (ML) and deep learning (DL) techniques. To the best of our knowledge, this study stands out as the first to solely focus on DL techniques, evaluating their efficacy and rationale in comparison to each other and to conventional ML methods. In this research, we offer a comprehensive classification of the characteristics utilized in deep learning research and provide information on the necessary preprocessing methods for creating appropriate training data for a deep learning model. We highlight the gaps identified in review papers discussing deep learning and machine learning studies, offering insights into future developments in this area. In general, deep learning methods have proven to be efficient in terms of computation and time for detecting social bots, demonstrating superior or comparable performance compared to traditional machine learning techniques.