Vickey Theodore, Breslin John G
College of Engineering & Informatics, National University of Ireland Galway, Galway, Ireland.
Department of Kinesiology, Point Loma University, San Diego, CA, United States.
JMIR Public Health Surveill. 2017 Oct 31;3(4):e82. doi: 10.2196/publichealth.8507.
Publicly available fitness tweets may provide useful and in-depth insights into the real-time sentiment of a person's physical activity and provide motivation to others through online influence.
The goal of this experimental approach using the fitness Twitter dataset is two-fold: (1) to determine if there is a correlation between the type of activity tweet (either workout or workout+, which contains the same information as a workout tweet but has additional user-generated information), gender, and one's online influence as measured by Klout Score and (2) to examine the sentiment of the activity-coded fitness tweets by looking at real-time shared thoughts via Twitter regarding their experiences with physical activity and the associated mobile fitness app.
The fitness tweet dataset includes demographic and activity data points, including minutes of activity, Klout Score, classification of each fitness tweet, the first name of each fitness tweet user, and the tweet itself. Gender for each fitness tweet user was determined by a first name comparison with the US Social Security Administration database of first names and gender.
Over 184 days, 2,856,534 tweets were collected in 23 different languages. However, for the purposes of this study, only the English-language tweets were analyzed from the activity tweets, resulting in a total of 583,252 tweets. After assigning gender to Twitter usernames based on the Social Security Administration database of first names, analysis of minutes of activity by both gender and Klout influence was determined. The mean Klout Score for those who shared their workout data from within four mobile apps was 20.50 (13.78 SD), less than the general Klout Score mean of 40, as was the Klout Score at the 95th percentile (40 vs 63). As Klout Score increased, there was a decrease in the number of overall workout+ tweets. With regards to sentiment, fitness-related tweets identified as workout+ reflected a positive sentiment toward physical activity by a ratio of 4 to 1.
The results of this research suggest that the users of mobile fitness apps who share their workouts via Twitter have a lower Klout Score than the general Twitter user and that users who chose to share additional insights into their workouts are more positive in sentiment than negative. We present a novel perspective into the physical activity messaging from within mobile fitness apps that are then shared over Twitter. By moving beyond the numbers and evaluating both the Twitter user and the emotions tied to physical activity, future research could analyze additional relationships between the user's online influence, the enjoyment of the physical activity, and with additional analysis a long-term retention strategy for the use of a fitness app.
公开可用的健身推文可能会提供有关个人体育活动实时情绪的有用且深入的见解，并通过网络影响力激励他人。
使用健身推特数据集的这种实验方法有两个目标：（1）确定活动推文的类型（锻炼或锻炼+，锻炼+包含与锻炼推文相同的信息，但有额外的用户生成信息）、性别以及通过Klout评分衡量的个人网络影响力之间是否存在关联；（2）通过查看推特上关于他们体育活动经历和相关移动健身应用程序的实时分享想法，来检查活动编码的健身推文的情绪。
健身推文数据集包括人口统计学和活动数据点，包括活动分钟数、Klout评分、每条健身推文的分类、每条健身推文用户的名字以及推文本身。通过将每条健身推文用户的名字与美国社会保障管理局的名字和性别数据库进行比较来确定性别。
在184天内，收集了23种不同语言的2856534条推文。然而，出于本研究的目的，仅对活动推文中的英语推文进行了分析，总共得到58325条推文。根据社会保障管理局的名字数据库为推特用户名分配性别后，确定了按性别和Klout影响力对活动分钟数的分析。从四个移动应用程序中分享锻炼数据的人的平均Klout评分为20.50（标准差13.78），低于一般Klout评分平均值40，第95百分位数的Klout评分也是如此（40对63）。随着Klout评分的增加，总体锻炼+推文的数量减少。关于情绪，被确定为锻炼+的健身相关推文对体育活动的积极情绪与消极情绪之比为4比1。
这项研究的结果表明，通过推特分享锻炼情况的移动健身应用程序用户的Klout评分低于一般推特用户，并且选择分享更多锻炼见解的用户在情绪上积极多于消极。我们提供了一个关于移动健身应用程序内体育活动信息传递的新视角，这些信息随后通过推特分享。通过超越数字并评估推特用户以及与体育活动相关的情绪，未来的研究可以分析用户的网络影响力、体育活动的乐趣之间的其他关系，并通过进一步分析制定健身应用程序使用的长期留存策略。
