Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise

TL;DR

This paper presents a novel framework using sentiment analysis and topic modeling to analyze 6 million tweets, revealing public negative sentiments and concerns related to diet, diabetes, exercise, and obesity, aiding health-related social media research.

Contribution

It introduces a new computational framework combining sentiment analysis and topic modeling to analyze large-scale Twitter data on health topics, enhancing understanding of public opinions on DDEO issues.

Findings

Negative sentiments align with health literature on DDEO

Identified prominent health-related topics in Twitter data

Framework demonstrates potential for public health sentiment analysis

Abstract

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.