  How to improve the quality of conversations in online communities has
attracted considerable attention recently. Having engaged, urbane, and reactive
online conversations has a critical effect on the social life of Internet
users. In this study, we are particularly interested in identifying a post in a
multi-party conversation that is unlikely to be further replied to, which
therefore kills that thread of the conversation. For this purpose, we propose a
deep learning model called the ConverNet. ConverNet is attractive due to its
capability of modeling the internal structure of a long conversation and its
appropriate encoding of the contextual information of the conversation, through
effective integration of attention mechanisms. Empirical experiments on
real-world datasets demonstrate the effectiveness of the proposal model. For
the widely concerned topic, our analysis also offers implications for improving
the quality and user experience of online conversations.
