Unsupervised Evaluation Method of Relative Coordination Degree from Group Perspective

In social media, coordinated groups spread false information and guide public opinion through organized social behaviors. Coordination detection methods based on behavioral sequences identify coordinated groups among users by extracting temporal and structural data. However, existing methods generally concentrate on identification and analysis from an individual perspective, lacking evaluation and detection from a group perspective to measure the degree of coordination, which results in low accuracy of detection results. To address these issues, we propose an unsupervised evaluation method of Relative Coordination Degree of Groups (RCDG). This method models the global time and differentiated network features from group perspective using Neural Time Point Process (NTPP) and Graph Contrastive Learning (GCL), and evaluates the relative degree of coordination based on the fused features. Experimental results show that RCDG can effectively evaluate the relative degree of coordination among groups. Furthermore, it can achieve an accuracy rate of up to 99.8% in detecting coordinated groups.