An Exploratory Study of (#)Exercise in the Twittersphere

TL;DR

This study uses social media analytics to identify and analyze exercise-related discussions on Twitter, revealing key topics and demonstrating the effectiveness of exploratory data analysis for health-related text data.

Contribution

It introduces a mixed-method approach combining data collection, topic modeling, and annotation to explore exercise discussions on Twitter, highlighting meaningful health-related topics.

Findings

86% of topics were meaningful after annotation

Physical activity was the most discussed topic (18.7%)

Exploratory data analysis effectively summarizes health-related social media data

Abstract

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.