Publication

Twitter classification model: the abc of two million fitness tweets

Vickey, Theodore A.
Ginis, Kathleen Martin
Dabrowski, Maciej
Repository DOI
Publication Date
2013-05-10
Type
Article
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Citation
Vickey, Theodore A. Ginis, Kathleen Martin; Dabrowski, Maciej (2013). Twitter classification model: the abc of two million fitness tweets. Translational Behavioral Medicine 3 (3), 304-311
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.
Funder
Publisher
Springer Nature
Publisher DOI
10.1007/s13142-013-0209-0
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland