Learning and Modeling the Underlying Semantics of Online Discussions

Abstract

User's online activities and their participation in social networks have increased significantly in recent years. As a result of this massive participation, the amount of user-generated contents (UGC) has also been expanded rapidly. There are many types of UGC; they can be as short as a comment, tweet or a Facebook post or as long as a review or blog post. In line with this rapid development of social networks, online discussions are growing as a popular and effective source of information for users because of their timely, lively and flexible content. Usually online discussions are developed and advanced incrementally by groups of users with various backgrounds and intents. However the flexibility, fast updating, informal languages and dynamic structure of online discussions make them a challenge for new users and automated systems to understand and learn the underlying semantics of these long threaded discussions.