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Leveraging Social Media for Health Promotion and Behavior Change: Methods of Analysis and Opportunities for Intervention

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Cognitive Informatics in Health and Biomedicine

Part of the book series: Health Informatics ((HI))

Abstract

This chapter describes methodologies used to describe, model, and predict user communication patterns in social media interactions, with the shared goal of facilitating understanding of health-related behavior change. To set the stage, the chapter presents an overview of the documented effects of social relationships on health behavior change. Investigators from a variety of disciplines have attempted to understand and harness these social ties for health promotion. Online communities, which digitize peer-to-peer communication, provide a unique opportunity to researchers to understand the mechanisms underlying human behavior change. Through transdisciplinary methods that draw upon socio-behavioral theories, and information and network sciences, analysis of communication patterns underlying social media user interactions is possible at scale. Such methods can provide insight into development of “healthier life” technologies that harness the power of social connections. Examples of such translational projects and implications for public health practice are discussed to conclude the chapter.

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Acknowledgements

Research reported in this publication was supported in part by the National Library of Medicine of the National Institutes of Health under Award Number 1R21LM012271-01, National Library of Medicine Grant Number 1R01LM011563, and UTHealth Innovation for Cancer Prevention Research Pre-doctoral Fellowship, The University of Texas School of Public Health-Cancer Prevention and Research Institute of Texas grant RP101503. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health and the Cancer Prevention and Research Institute of Texas. We would like to express our sincere gratitude to our collaborator Dr. Nathan K. Cobb for providing us with de-identified data from QuitNet platform. We would like to thank Tom Landauer for providing us with the TASA corpus, and contributors to the Semantic Vectors open source package, in particular Adrian Kuhn and David Erni, the contributors of the sparse SVD implementation we used for the LSA package.

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  1. School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St, Suite 165, Houston, TX, 77584, USA

    Sahiti Myneni Ph.D., M.S.E. & Trevor Cohen M.B.Ch.B., Ph.D.

  2. Division of Health Promotion and Behavioral Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

    Kayo Fujimoto Ph.D.

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  1. Sahiti Myneni Ph.D., M.S.E.
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  2. Kayo Fujimoto Ph.D.
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  3. Trevor Cohen M.B.Ch.B., Ph.D.
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Correspondence to Sahiti Myneni Ph.D., M.S.E. .

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  1. The New York Academy of Medicine, New York, NY, USA

    Vimla L. Patel

  2. University of Waterloo, Waterloo, ON, Canada

    Jose F. Arocha

  3. Weill Cornell Medical College, New York, NY, USA

    Jessica S. Ancker

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Myneni, S., Fujimoto, K., Cohen, T. (2017). Leveraging Social Media for Health Promotion and Behavior Change: Methods of Analysis and Opportunities for Intervention. In: Patel, V., Arocha, J., Ancker, J. (eds) Cognitive Informatics in Health and Biomedicine. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-51732-2_15

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