2017/12/22 by Yunhao Jiao, Cheng Li, Jiao, Yunhao +5 · 1 citation
Computer Science · Psychology · #Computation and Language (cs.CL) #Digital Communication and Language #FOS: Computer and information sciences #Humor Studies and Applications #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1712.08636
openalex publication_date 2017/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.