research-article

Predicting User Posting Activities in Online Health Communities with Deep Learning

Authors:

Nick StreetAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 11, Issue 3

Article No.: 12, Pages 1 - 15

https://doi.org/10.1145/3383780

Published: 21 July 2020 Publication History

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.

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Index Terms

Predicting User Posting Activities in Online Health Communities with Deep Learning
1. Human-centered computing
2. Information systems
  1. Information systems applications

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 11, Issue 3

Special Section on WITS 2018 and Regular Articles

September 2020

140 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3407737

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Copyright © 2020 ACM.

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Publication History

Published: 21 July 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 November 2019

Received: 01 June 2019

Published in TMIS Volume 11, Issue 3

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Deng SSprangers OLi MSchelter Sde Rijke M(2024)Domain Generalization in Time Series ForecastingACM Transactions on Knowledge Discovery from Data10.1145/364303518:5(1-24)Online publication date: 31-Jan-2024
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