Identifying conversational message threads by integrating classification and data clustering

Conversational message thread identification regards a wide spectrum of applications, ranging from social network marketing to virus propagation, digital forensics, etc. Many different approaches have been proposed in literature for the identification of conversational threads focusing on features that are strongly dependent on the dataset. In this paper, we introduce a novel method to identify threads from any type of conversational texts overcoming the limitation of previously determining specific features for each dataset. Given a pool of messages, our method extracts and maps in a three dimensional representation the semantic content, the social interactions and the timestamp; then it clusters each message into conversational threads. We extend our previous work by introducing a deep learning approach and by performing new extensive experiments and comparisons with classical learning algorithms.