Persistent sharing of fitness app status on twitter

Kunwoo Park, Ingmar Weber, Meeyoung Cha, Chul Lee

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

18 Citations (Scopus)

Abstract

As the world becomes more digitized and interconnected, information that was once considered to be private such as one's health status is now being shared publicly. To understand this new phenomenon better, it is crucial to study what types of health information are being shared on social media and why, as well as by whom. In this paper, we study the traits of users who share their personal health and fitness related information on social media by analyzing fitness status updates that MyFitnessPal users have shared via Twitter. We investigate how certain features like user profile, fitness activity, and fitness network in social media can potentially impact the longterm engagement of fitness app users. We also discuss implications of our findings to achieve a better retention of these users and to promote more sharing of their status updates.

Original languageEnglish
Title of host publicationProceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
PublisherAssociation for Computing Machinery
Pages184-194
Number of pages11
Volume27
ISBN (Print)9781450335928
DOIs
Publication statusPublished - 27 Feb 2016
Event19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2016 - San Francisco, United States
Duration: 27 Feb 20162 Mar 2016

Other

Other19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2016
CountryUnited States
CitySan Francisco
Period27/2/162/3/16

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Keywords

  • Fitness engagement
  • Social sharing

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this

Park, K., Weber, I., Cha, M., & Lee, C. (2016). Persistent sharing of fitness app status on twitter. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW (Vol. 27, pp. 184-194). Association for Computing Machinery. https://doi.org/10.1145/2818048.2819921