Exploring Diabetes and Users\u27 Lifestyle Choices in Digital Spaces to Improve Health Outcomes

The information derived from social media analytic studies provides valuable sources of information for healthcare stakeholders. However, there is still a lack of research with using social media to identify the lifestyle choices of those dealing with diabetes in order to better understand and design impactful health interventions before an extremity like death occurs due to diabetes. This exploratory study aims to demonstrate how social media can be leveraged as a data source to help us understand the lifestyle choices of those dealing with diabetes. Using two text mining approaches - sentiment analysis and unsupervised topic modeling - food and physiology were topics expressed in both sentiments. Overall, lifestyle related topics accounted for nearly 25% of the topics identified in the corpus of data. There is a pressing need for incorporating predictive modelling approaches to this study in order to quantify our findings and how this knowledge can improve health outcomes from a population perspective

Exploring the Use of Twitter by Leading Medical Centers in the United States

Healthcare organizations such as hospitals and clinics increasingly use social media platforms such as Twitter to raise awareness in the community about health and wellness issues. In line with this trend, the current study seeks to understand how Cleveland Clinic, Johns Hopkins Hospital, and Mayo Clinic use their primary Twitter accounts to post information related to each of the following major health topics: brain, cancer, diabetes, diet, exercise, heart, mental health, and obesity. Nearly 6,000 tweets posted by those medical centers were collected and analyzed. The results showed that the three medical centers used diet and cancer topics more frequently than the other six topics in their tweets. Moreover, diabetes was consistently the least frequently used topic in the analyzed tweets. The results associated with the other five topics were mixed

CHARACTERIZING DISEASES AND DISORDERS IN GAY USERS’ TWEETS

A lack of information exists about the health issues of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people who are often excluded from national demographic assessments, health studies, and clinical trials. As a result, medical experts and researchers lack holistic understanding of the health disparities facing these populations. Fortunately, publicly available social media data such as Twitter data can be utilized to support the decisions of public health policy makers and managers with respect to LGBTQ people. This research employs a computational approach to collect tweets from gay users on healthrelated topics and model these topics. To determine the nature of health-related information shared by men who have sex with men on Twitter, we collected thousands of tweets from 177 active users. We sampled these tweets using a framework that can be applied to other LGBTQ sub-populations in future research. We found 11 diseases in 7 categories based on ICD 10 that are in line with the published studies and official reports

An exploratory study of (#)exercise in the Twittersphere

Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings, 86% of the detected topics were identified as meaningful topics after conducting the data annotation process. The most discussed exercise related topics were physical activity (18.7%), lifestyle behaviors (6.6%), and dieting (4%). The results from our experiment indicate that the exploratory data analysis is an effective approach to summarizing the various characteristics of text data for different health and medical applications

SOCIAL MEDIA ANALYSIS FOR ORGANIZATIONS: US NORTHEASTERN PUBLIC AND STATE LIBRARIES CASE STUDY

Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of tweets posted by nine public libraries in the northeastern United States of America. In December 2017, this study extracted more than 19,000 tweets from the Twitter accounts of seven state libraries and two urban public libraries. Computational methods were applied to collect the tweets and discover meaningful themes. This paper shows how the libraries have used Twitter to represent their services and provides a starting point for different organizations to evaluate the themes of their public tweets