Before revision:  In contrast , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes. Furthermore, we leverage the learned model to understand and explain the behaviours of coordinated accounts in disinformation campaigns. We find that the average influence between coordinated accountsis the highest, whereas these accounts are not much influenced by regular accounts. We evaluate the effectiveness of the proposed method on Twitter data related to Russian interference in US Elections.
After revision:  In contrast , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and hidden group behaviours based on Temporal Point Processes (TPP) and Gaussian Mixture Model (GMM), to capture inherent characteristics of coordination which is, accounts that coordinate must strongly influence each other's activities, and collectively appear anomalous from normal accounts. To address the challenges of optimizing the proposed model, we provide a bilevel optimization algorithm with theoretical guarantee on convergence. We verified the effectiveness of the proposed method on Twitter data related to Russian interference in US Elections.