Enhanced Bot Detection on TwiBot-20 Dataset

Bot detection is critical in safeguarding social networks against malicious activities such as propagating misinformation and shaping public opinion. Twitter, being extensively studied due to its accessibility and interactive nature, serves as an ideal platform for studying bot behaviors. In this study, we evaluate various network configurations using TwiBot-20 dataset to assess their efficacy in bot detection. Our investigation incorporates Graph Neural Networks (GNNs) models to leverage both network structure and textual content for enhanced detection accuracy. Experimental results demonstrate our combined model's superior performance, achieving high accuracy and robust metrics across the board. This research contributes to advancing bot detection methodologies, aiming to fortify social network integrity against emerging threats.