基于细粒度特征权重专家网络的社交机器人检测方法OA

In recent years,research in the field of social bot detection has gradually evolved from individual feature analysis to group feature mining,and from traditional feature engineering to deep learning methods.Among them,graph network-based methods have shown significant advantages:they can integrate account behavior features,text semantic features,and network topology features,converting social bot detection into a graph node classification task.However,most existing detection methods adopt general models for detection and fail to consider the differences in fine-grained features among different types of social bots,which limits the detection accuracy across business scenarios.Based on this,a social bot detection method based on a fine-grained feature expert attention mechanism was proposed.The method constructed a business expert network,enabling each expert to focus on learning differentiated weight combinations of fine-grained features.Through multi-expert feature fusion and comprehensive analysis,it achieved integrated detection of potentially multi-type social bots across various business scenarios.Experimental results on a public Twitter dataset for social bot detection demonstrated that this method outperformed existing mainstream detection methods,with a relative improvement of 1.52% in the F1-score.