2018/12/08 by George Shaw, Amir Karami, Shaw, George +1
Computer Science · Mathematics · Psychology · Social Sciences · #Applications (stat.AP) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Mental Health via Writing #Sentiment Analysis and Opinion Mining #Social Media in Health Education #cs.CL #cs.CY #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1812.03260
arxiv created 2018/12/08 · openalex publication_date 2018/12/08 · arxiv updated 2018/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.