Disinformation campaigns on social media, involving coordinated activities from malicious accounts towards manipulating public opinion, have become increasingly prevalent. There has been growing evidence of social media abuse towards influencing politics and social issues in other countries, raising numerous concerns. The identification and prevention of coordinated campaigns has become critical to tackling disinformation at its source. Existing approaches to detect malicious campaigns make strict assumptions about coordinated behaviours, such as malicious accounts perform synchronized actions or share features assumed to be indicative of coordination. Others require part of the malicious accounts in the campaign to be revealed in order to detect the rest. Such assumptions significantly limit the effectiveness of existing approaches. 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 accounts is 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. Additionally, we identify disinformation campaigns in COVID-19 data collected from Twitter, and provide the first evidence and analysis of existence of coordinated disinformation campaigns in the ongoing pandemic.