Journal
WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018)
Volume -, Issue -, Pages 1145-1154Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3178876.3186013
Keywords
Online conversations; conversation prediction; deep learning
Funding
- National Science Foundation [1054199, 1633370, 1131500, 1620319]
- Chinese 973 program [2015CB352302]
- NSFC [U1611461]
- key program of Zhejiang Province [2015C01027]
- Direct For Social, Behav & Economic Scie
- Divn Of Social and Economic Sciences [1131500] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1054199] Funding Source: National Science Foundation
- SBE Off Of Multidisciplinary Activities
- Direct For Social, Behav & Economic Scie [1620319] Funding Source: National Science Foundation
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How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, civil, 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 proposed model. For the widely concerned topic, our analysis also offers implications for how to improve the quality and user experience of online conversations, or how to engage users in a conversation with a chatbot.
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