Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise

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 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.

Exploring diseases and syndromes in neurology case reports from 1955 to 2017 with text mining

“…LDA has been utilized for health applications such as diet, diabetes, exercise, and obesity [28,29,30], and LGBT health issues [31,32], and non-health applications such as business and organizations [33,34,35], spam detection [36], disaster management [37], and politics [38,39]. There are some work investigating related studies in medical and health domains using LDA such as exploring the literature of depressive disorders [17], biomedical literature [40,18], and adolescent substance use and depression [41].…”

Political Popularity Analysis in Social Media

“…Among social media, Twitter with millions of tweets per day has provided a cost-effective data access platform for collecting millions of tweets containing feelings and opinions to facilitate social media research [48]. Twitter data has been used in different political applications election analysis [20] and non-political applications such as business [15], libraries [8,21], social bot analysis [32], and health like analyzing diabetes, diet, obesity [22,49], exercise [50], LGBT health [62,28]. However, this data has not been considered for popularity analysis.…”

Limiting the Influence to Vulnerable Users in Social Networks: A Ratio Perspective

“…A main drawback of influence maximization is that it is unethical to influence users many of whom could be harmed due to their demographics, health conditions, or socioeconomic profile [7]. The users who could be harmed are referred to as vulnerable and are identified based on domain knowledge (e.g., user message content and sentiment analysis) [15,21]. For example, when an organization aims to promote alcoholic beverages, it should avoid influencing users many of whom have drinking problems.…”