Twitter classification model: the ABC of two million fitness tweets
Twitter classification model: the ABC of two million fitness tweets
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DOI：
10.1007/s13142-013-0209-0
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发表时间：
2013-09-01
影响因子：
3.6
通讯作者：
Dabrowski, Maciej
中科院分区：
文献类型：
--
作者：
Vickey, Theodore A.;Ginis, Kathleen Martin;Dabrowski, Maciej
该项目的目的是设计和测试数据收集和管理工具，可用于研究在体育活动的背景下使用移动的健身应用程序和社交网络。该项目进行了6个月，涉及从5个移动的健身应用程序（Nike+、RunKeeper、MyFitnessApp、Endomondo和dailymile）收集共享的Twitter数据。在此期间，使用在线推文收集应用程序和定制的JavaScript收集、处理和分类了超过280万条推文。使用扎根理论，开发了一个分类模型来分类和理解应用程序用户共享的信息类型。我们的数据显示，通过跟踪移动的健身应用标签，可以收集大量信息，包括但不限于日常使用模式、锻炼频率、基于位置的锻炼和整体锻炼情绪。
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.