A Social Bot Detection Method Using Multi-features Fusion and Model Optimization Strategy

Online Social Networks (OSNs) have become an indispensable part of our lives, providing a platform for users to access and share information. However, the emergence of malicious social bots has disrupted the normal functioning of OSNs, posing a threat to their healthy development. With the evolution of social bots making them increasingly difficult to distinguish from human users, social bot detection has become a significant challenge. The difficulty lies in constructing effective features that cater to detect multiple types of social bots, performance of a single model in detecting social bots, and handling the imbalanced distribution of social bots and human users in real environments. To address these challenges, this paper proposes a social bot detection method based on multi-features fusion and model optimization strategy. The proposed method analyzes differences in user profile, tweets content, temporal information, and activity behaviors to extract and fuse effective features. Weighted soft voting mechanism and a searching best detection threshold strategy are creatively introduced to improve the performance of model. The superiority of our method is confirmed on four real datasets. The effectiveness of different dimensional features on detecting social bots is also analyzed. Furthermore, our method achieves better performance on imbalanced datasets, indicating great robustness and its ability to detect social bots in real environments.