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Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and Temporality

Akbari, M; Hu, X; Wang, F; Chua, T-S; (2017) Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and Temporality. IEEE Transactions on Knowledge and Data Engineering , 29 (10) pp. 2360-2373. 10.1109/TKDE.2017.2722411. Green open access

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Abstract

The increasing popularity of social media has encouraged health consumers to share, explore, and validate health and wellness information on social networks, which provide a rich repository of Patient Generated Wellness Data (PGWD). While data-driven healthcare has attracted a lot of attention from academia and industry for improving care delivery through personalized healthcare, limited research has been done on harvesting and utilizing PGWD available on social networks. Recently, representation learning has been widely used in many applications to learn low-dimensional embedding of users. However, existing approaches for representation learning are not directly applicable to PGWD due to its domain nature as characterized by longitudinality, incompleteness, and sparsity of observed data as well as heterogeneity of the patient population. To tackle these problems, we propose an approach which directly learns the embedding from longitudinal data of users, instead of vector-based representation. In particular, we simultaneously learn a low-dimensional latent space as well as the temporal evolution of users in the wellness space. The proposed method takes into account two types of wellness prior knowledge: (1) temporal progression of wellness attributes; and (2) heterogeneity of wellness attributes in the patient population. Our approach scales well to large datasets using parallel stochastic gradient descent. We conduct extensive experiments to evaluate our framework at tackling three major tasks in wellness domain: attribute prediction, success prediction, and community detection. Experimental results on two real-world datasets demonstrate the ability of our approach in learning effective user representations.

Type: Article
Title: Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and Temporality
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TKDE.2017.2722411
Publisher version: https://doi.org/10.1109/TKDE.2017.2722411
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Social network services, Sociology, Statistics, Matrix decomposition, Diabetes, Data models, Latent space learning, user’s wellness, social networks
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10062770
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