Crowdsourcing health labels: Inferring body weight from profile pictures

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

To use social media for health-related analysis, one key step is the detection of health-related labels for users. But unlike transient conditions like u, social media users are less vocal about chronic conditions such as obesity, as users might not tweet \I'm still overweight". As, however, obesity-related conditions such as diabetes, heart disease, osteoarthritis, and even cancer are on the rise, this obese-or-not label could be one of the most useful for studies in public health. In this paper we investigate the feasibility of using profile pictures to infer if a user is overweight or not. We show that this is indeed possible and further show that the fraction of labeled-As-overweight users is higher in U.S. counties with higher obesity rates. Going from public to individual health analysis, we then find differences both in behavior and social networks, for example finding users labeled as overweight to have fewer followers.

Original languageEnglish
Title of host publicationDH 2016 - Proceedings of the 2016 Digital Health Conference
PublisherAssociation for Computing Machinery, Inc
Pages105-109
Number of pages5
ISBN (Print)9781450342247
DOIs
Publication statusPublished - 11 Apr 2016
Event6th International Conference on Digital Health, DH 2016 - Montreal, Canada
Duration: 11 Apr 201613 Apr 2016

Other

Other6th International Conference on Digital Health, DH 2016
CountryCanada
CityMontreal
Period11/4/1613/4/16

Keywords

  • BMI
  • Obesity
  • Profile Images
  • Social Network Analysis
  • Twitter

ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications
  • Health Informatics

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  • Cite this

    Weber, I., & Mejova, Y. (2016). Crowdsourcing health labels: Inferring body weight from profile pictures. In DH 2016 - Proceedings of the 2016 Digital Health Conference (pp. 105-109). Association for Computing Machinery, Inc. https://doi.org/10.1145/2896338.2896343