Characterizing diabetes, diet, exercise, and obesity comments on Twitter

Abstract

Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO); however, collecting and analyzing a large scale conversational public health data set is a challenging research task. The goal of this research is to analyze the characteristics of the general public's opinions in regard to diabetes, diet, exercise and obesity (DDEO) as expressed on Twitter. A multi-component semantic and linguistic framework was developed to collect Twitter data, discover topics of interest about DDEO, and analyze the topics. From the extracted 4.5 million tweets, 8% of tweets discussed diabetes, 23.7% diet, 16.6% exercise, and 51.7% obesity. The strongest correlation among the topics was determined between exercise and obesity (p < .0002). Other notable correlations were: diabetes and obesity (p < .0005), and diet and obesity (p < .001). DDEO terms were also identified as subtopics of each of the DDEO topics. The frequent subtopics discussed along with "Diabetes", excluding the DDEO terms themselves, were blood pressure, heart attack, yoga, and Alzheimer. The non-DDEO subtopics for "Diet" included vegetarian, pregnancy, celebrities, weight loss, religious, and mental health, while subtopics for "Exercise" included computer games, brain, fitness, and daily plan. Non-DDEO subtopics for "Obesity" included Alzheimer, cancer, and children. With 2.67 billion social media users in 2016, publicly available data such as Twitter posts can be utilized to support clinical providers, public health experts, and social scientists in better understanding common public opinions in regard to diabetes, diet, exercise, and obesity.

#Cyberbullying in the digital age: Exploring people's opinions with text mining

“…The current study aims to reveal people's insights about cyberbullying from tweets. Twitter has become a valuable source of information to explore peoples' behaviors and viewpoints about various public health and social issues (Karami, Dahl, Turner‐McGrievy, Kharrazi, & Shaw Jr, ). However, few studies (e.g., McHugh, Saperstein, & Gold, ) have used Twitter as a data source to explore people's viewpoints on cyberbullying.…”

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].…”

Characterizing transgender health issues in Twitter

“…Social media provides a new context in which to understand the unique experiences of sexual and gender minorities, including transgender people. Although prior research has used Twitter data for health applications, including intestinal disease (Zou, Lampos, Gorton, & Cox, 2016) and diabetes, diet, exercise, and obesity (Karami, Dahl, Turner-McGrievy, Kharrazi, & Shaw, 2018), these data have not been utilized to study the health information needs of transgender populations. This paper addresses this gap in the literature and proposes a computational framework using text mining methods to explore tweets of transgender users and identify the differences in the health-related information needs between TM and TW by analyzing their tweets using linguistics analysis and topic modeling.…”