Journal article
Predicting User Posting Activities in Online Health Communities with Deep Learning
ACM transactions on management information systems, Vol.11(3), pp.1-15
08/01/2020
DOI: 10.1145/3383780
Abstract
Online health communities (OHCs) represent a great source of social support for patients and their caregivers. Better predictions of user activities in OHCs can help improve user engagement and retention, which are important to manage and sustain a successful OHC. This article proposes a general framework to predict OHC user posting activities. Deep learning methods are adopted to learn from users' temporal trajectories in both the volumes and content of posts published over time. Experiments based on data from a popular OHC for cancer survivors demonstrate that the proposed approach can improve the performance of user activity predictions. In addition, several topics of users' posts are found to have strong impact on predicting users' activities in the OHC.
Details
- Title: Subtitle
- Predicting User Posting Activities in Online Health Communities with Deep Learning
- Creators
- Xiangyu Wang - University of IowaKang Zhao - University of IowaXun Zhou - University of IowaNick Street - University of Iowa
- Resource Type
- Journal article
- Publication Details
- ACM transactions on management information systems, Vol.11(3), pp.1-15
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/3383780
- ISSN
- 2158-656X
- eISSN
- 2158-6578
- Number of pages
- 15
- Grant note
- 71572013 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Language
- English
- Date published
- 08/01/2020
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380429302771
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