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Twitter classification model: the ABC of two million fitness tweets

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Translational Behavioral Medicine

ABSTRACT

The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.8 million tweets were collected, processed, and categorized using an online tweet collection application and a customized JavaScript. Using the grounded theory, a classification model was developed to categorize and understand the types of information being shared by application users. Our data show that by tracking mobile fitness app hashtags, a wealth of information can be gathered to include but not limited to daily use patterns, exercise frequency, location-based workouts, and overall workout sentiment.

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Authors and Affiliations

  1. National University of Ireland Galway, Galway, Ireland

    Theodore A. Vickey & Maciej Dabrowski PhD

  2. McMaster University, Hamilton, Canada

    Kathleen Martin Ginis PhD

Authors
  1. Theodore A. Vickey
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  2. Kathleen Martin Ginis PhD
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  3. Maciej Dabrowski PhD
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Corresponding author

Correspondence to Theodore A. Vickey.

Additional information

Implications

Practice: The fitness tweet classification model can be used by researchers to better understand and classify fitness information collected via Twitter.

Policy: A system was developed whereby policy decisions can be made more effectively by the classification of real-time, on-body data collection rather than self-reported measures.

Research: This study provides a research opportunity between health and exercise science and social networking/social software disciplines.

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Vickey, T.A., Ginis, K.M. & Dabrowski, M. Twitter classification model: the ABC of two million fitness tweets. Behav. Med. Pract. Policy Res. 3, 304–311 (2013). https://doi.org/10.1007/s13142-013-0209-0

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  • DOI: https://doi.org/10.1007/s13142-013-0209-0

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