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Title:An exploratory study of (#)exercise in the Twittersphere
Author(s):Karami, Amir; Shaw, George
Subject(s):Twitter
Text mining
Topic modeling
Health informatics
Exercise
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 an effective approach to summarizing the various characteristics of text data for different health and medical applications.
Issue Date:2019-03-15
Publisher:iSchools
Series/Report:iConference 2019 Proceedings
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/103327
DOI:https://doi.org/10.21900/iconf.2019.103327
Rights Information:Copyright 2019 Amir Karami and George Shaw
Date Available in IDEALS:2019-03-22


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