  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.
