Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise
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 six 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.
Digital Object Identifier (DOI)
Proceedings of the 80th Annual Meeting of the Association for Information Science and Technology (ASIST), 2017.
© Association for Information Science and Technology, 2017
Shaw, G. Jr, Karami A. (2017). Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise. Proceedings of the 80th Annual Meeting of the Association for Information Science and Technology (ASIST). Crystal City, VA. https://doi.org/10.1002/pra2.2017.14505401039