An Exploratory Study of (#)Exercise in the Twittersphere
This incremental work addresses the need for summarizing text data characteristics for health and medical applications using social media analytics.
The study tackled the problem of analyzing exercise-related patterns in Twitter data using a mixed-method approach, finding that 86% of detected topics were meaningful, with physical activity (18.7%), lifestyle behaviors (6.6%), and dieting (4%) as the most discussed topics.
Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings, 86% of the detected topics were identified as meaningful topics after conducting the data annotation process. The most discussed exercise-related topics were physical activity (18.7%), lifestyle behaviors (6.6%), and dieting (4%). The results from our experiment indicate that the exploratory data analysis is a practical approach to summarizing the various characteristics of text data for different health and medical applications.