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Predicting User Posting Activities in Online Health Communities with Deep Learning

Predicting User Posting Activities in Online Health Communities with Deep Learning 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Predicting User Posting Activities in Online Health Communities with Deep Learning

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 ACM
ISSN
2158-656X
eISSN
2158-6578
DOI
10.1145/3383780
Publisher site
See Article on Publisher Site

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.

Journal

ACM Transactions on Management Information Systems (TMIS)Association for Computing Machinery

Published: Jul 21, 2020

Keywords: Predictive model

References