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Syst."],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>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\u2019 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. 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