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Predicting Community Health Through Heterogeneous Social Networks

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Abstract

Social media has been utilized as a measurement tool of population health outcomes. Popular social media platforms are heterogeneous that allow users to share multiple types of links and data. However, existing studies on social media-based population analysis largely focus on leveraging user-generated text, while other data sources are under-explored. In this work, we examine the use of two typical data sources from online heterogeneous social networks: text as conventional and images as a novel sensor in understanding population health. To make use of visual content, we propose two types of population health representations extracted from shared images, in particular color histograms—a set of hand-crafted visual features, and automatic features learned from a deep convolutional neural network. For textual data, we adopt two well-known textual feature types: language style features and content-based features. To deal with the problem of weakly labeled data, we apply the multi-instance learning technique to reduce probable classification bias. For evaluation, we benchmark the proposed methods on the task of predicting the US county health outcomes through a large-scale dataset collected from Flickr. The experimental results suggest that social media visual content, in addition to data, is an informative indicator for population health outcomes. These experiments along with in-depth analysis will serve as a technical documentation for future research on population health analysis through the lens of heterogeneous social media.

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Data availability

The data that support the findings of this study are not openly available due to human data and are available from the corresponding author upon reasonable request.

Notes

  1. https://www.cdc.gov/chronicdisease/resources/publications/aag/brfss.htm.

  2. https://www.cdc.gov/brfss/.

  3. https://www.cdc.gov/brfss/index.html.

  4. http://www.okilab.es/how-big-data-is-changing-healthcare/.

  5. https://www.flickr.com/services/api/.

  6. https://www.census.gov/geo/reference/codes/cou.html.

  7. https://www.cdc.gov/brfss/index.html.

  8. https://www.cdc.gov/brfss/questionnaires/pdf-ques/2014_BRFSS.pdf.

  9. https://keras.io.

  10. https://www.tensorflow.org.

  11. http://deeplearning.net/software/theano.

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Authors and Affiliations

  1. Faculty of Information Technology, Nha Trang University, Nha Trang, Vietnam

    Hung Nguyen & Hang Le

  2. Applied Artificial Intelligence Institute, Deakin University, Burwood, Australia

    Hung Nguyen

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This article is part of the topical collection “Advanced Computing Systems and Analytics for IoT-enabled, AI-powered Smart Society” guest edited by Lam-Son Lê and Maurizio Marchese.

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Nguyen, H., Le, H. Predicting Community Health Through Heterogeneous Social Networks. SN COMPUT. SCI. 4, 227 (2023). https://doi.org/10.1007/s42979-023-01718-z

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