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Social Web and Health Research pp 139–169Cite as

Learning Wellness Profiles of Users on Social Networks: The Case of Diabetes

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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. This chapter focuses on wellness profiling of users where we demonstrate algorithms to effectively harvest social media to extract wellness information of individuals as well as construct the latent profile of users. In particular, we study the wellness profile of users in diabetes, with extension to obesity and depression.

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Notes

  1. 1.

    http://www.pewinternet.org/2010/03/24/social-media-and-health/

  2. 2.

    http://www.cdc.gov/chronicdisease/overview/

  3. 3.

    https://www.un.org/en/ga/ncdmeeting2011/

  4. 4.

    This is a real tweet from the dataset.

  5. 5.

    Note that the numbers in Table 8.2 do not add up to 11; 217 since our dataset is a multi-label dataset meaning that some messages discuss about more than one PWE.

  6. 6.

    In this text, we use wellness feature (e.g., blood glucose, hypertension) and wellness events (onset of asthma attack, hyperglycemia) interchangeably.

  7. 7.

    We followed a bootstrapping approach similar to [68] to ensure the coverage and diversity of used patterns, where all extracted patterns are manually verified to ensure accuracy.

  8. 8.

    In our dataset, there are three non-common diabetes types: gestational diabetes, diabetes LADA (Type 1.5), and diabetes insipidus.

  9. 9.

    Due to user privacy concerns, some words/sentences may be different from original version.

  10. 10.

    We did not consider the first week of May and the last week of October because the data was partially crawled.

  11. 11.

    http://instagram.com

  12. 12.

    http://endomondo.com

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Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, London, UK

    Mohammad Akbari

  2. Computer Science and Engineering Department, Texas A&M University, College Station, TX, USA

    Xia Hu

  3. Department of Computer Science School of Computing, National University of Singapore, Singapore, Singapore

    Tat-Seng Chua

Authors
  1. Mohammad Akbari
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  2. Xia Hu
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  3. Tat-Seng Chua
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Corresponding author

Correspondence to Mohammad Akbari .

Editor information

Editors and Affiliations

  1. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Jiang Bian

  2. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Yi Guo

  3. School of Information, Florida State University, Tallahassee, FL, USA

    Zhe He

  4. Computer Science and Engineering Department, Texas A&M University, College Station, TX, USA

    Xia Hu

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Akbari, M., Hu, X., Chua, TS. (2019). Learning Wellness Profiles of Users on Social Networks: The Case of Diabetes. In: Bian, J., Guo, Y., He, Z., Hu, X. (eds) Social Web and Health Research. Springer, Cham. https://doi.org/10.1007/978-3-030-14714-3_8

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