2017/09/22 by George Shaw, George Shaw Jr., Amir Karami +2
Computer Science · Mathematics · Social Sciences · #Applications (stat.AP) #Computation (stat.CO) #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Machine Learning (stat.ML) #Sentiment Analysis and Opinion Mining #Social Media in Health Education #Social and Information Networks (cs.SI) #cs.CL #cs.SI #stat.AP #stat.CO #stat.ML
paper · pdf · doi:10.48550/arxiv.1709.07915
The 2017 Annual Meeting of the Association for Information Science and Technology (ASIST)
arxiv created 2017/09/22 · openalex publication_date 2017/09/22 · arxiv updated 2017/09/26 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.