Social Media

Major depressive disorder has a lifetime prevalence of 16.6% in the United States. Social media platforms – e.g. Twitter, Facebook, Reddit – are potential resources for better understanding and monitoring population-level mental health status over time. Based on DSM-5 diagnostic criteria, our research aims to develop a natural language processing-based system for monitoring major depressive disorder at the population-level using public social media data.

Objective

We aim to develop an annotation scheme and corpus of depression-related tweets to serve as a test-bed for the development of natural language processing algorithms capable of automatically identifying depression-related symptoms from Twitter feeds.

Despite numerous successes in using social media to detect food borne illness and to predict influenza trends, the use of social media as a public health tool has yet to gain widespread adoption. While social media data cannot directly diagnose illness, aggregate trends in symptom proliferation may readily be observed. Such trends may allow a health agency to watch for signs and symptoms related to target conditions within its jurisdiction. Further, social media surveillance offers a distinct advantage in immediacy and sensitivity as it is not dependent upon infected individuals seeking care for reportable illnesses and as such information is not delayed by the handling, transfer, and processing of reports. These advantages may enable the earlier preparation and initiation of scaled response sequences during public health emergencies. Such data may also yield additional evidence through shared symptoms, rumors, and observations crucial to an epidemiological investigation.

Objective

To formally introduce ChatterGrabber, an open source, natural language processing based toolset for public health social media surveillance. ChatterGrabber is designed to collect and categorize a high volume of content at a low cost, providing a readily deployable solution for Epidemiologists to track emergent outbreaks in the field and a signal for syndromic surveillance.

 

Influenza-like illness (ILI) remains a significant public health burden to both the general public and the U.S. Department of Defense. Military personnel are especially susceptible to disease outbreaks owing to the often-crowded living quarters, substantial geographic movement, and physical stress placed upon them. Currently, the military employs syndromic surveillance on electronic reporting of clinical diagnoses. While faster than traditional, biologically-focused monitoring techniques, the military surveillance system proved inadequate at detecting outbreaks quickly enough in a recent study conducted by the CDC. Recently, research has included novel data sources, like social media, to conduct disease detection in real-time and capture communities not traditionally accounted for in current surveillance systems. Data-mining techniques are used to identify influenza-related social media posts and train a model against validated medical data. By integrating social media data and a medical dataset of all ILI-related laboratory specimens and doctor visits for the entire military cohort, a more comprehensive model than presently exists for disease identification and transmission will be possible.

Objective

To integrate existing influenza surveillance data sources and social media data into an accurate and timely outbreak detection model embedded into dashboard biosuveillance analytics for the Department of Defense.

Previous research identifies social media as an informal source of near-real time health data that may add value to disease surveillance systems by providing broader access to health data across hard-toreach populations. This indirect health monitoring may improve public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. The Philippines consists of over 7,000 islands and is prone to meteorological (storms), hydrological (floods), and geophysical disasters (earthquakes and volcanoes). In these situations, evacuation centers are used for safety and medical attention and often house up to 50K people each for 2 or more months, sometimes with unclean water sources and improper sanitation. Consequently, these conditions are a perfect venue for communicable disease transmission and have been proposed to cause disease outbreaks weeks after the original disaster occurred. Coined the social media capital of the world1, the Philippines provides a perfect opportunity to evaluate the potential of social media use in disease surveillance.

Objective

To determine the potential of Twitter data as an early warning of a likely communicable disease outbreak following a natural disaster, and if successful, develop an open-source algorithm for use by interested parties.

 Numerous methods using social media for syndromic surveillance and disease tracking have been developed. Many websites use Twitter and other social media to track specific diseases or syndromes.1 Many are intended for public use and the extent of use by public health agencies is limited.2 Our work builds on 4 years of experience by our multi-disciplinary team3 with a focus on local surveillance of influenza. 4,5

Objective

Create a flexible user-friendly geo-based social media analytic tool for local public health professionals. With the goal of increasing situational awareness, system has capability to process, sort and display tweets with text terms of potential public health interest. We continue to refine the Social Media and Research Testbed (SMART) via feedback from surveillance professionals.

The success of public health campaigns in decreasing or eliminating the burden of vaccine-preventable diseases can be undermined by media content influencing vaccine hesitancy in the population. A tool for tracking and describing the ever-growing platforms for such media content can help decide how and where to invest in campaigns to increase public confidence in vaccines. The Vaccine Sentimeter, developed from the Healthmap project, aims to assist public health practitioners in maintaining or improving vaccine coverage through a real-time, online visualization tool of global media content on vaccines.

Objective The current analysis describes the scope and trends in United States content from the Vaccine Sentimeter’s results, while seeking to examine any possible links between media content, vaccine coverage, and reported vaccine adverse events in the country.

Internet based technologies are becoming quite prominent among today’s generation due to its easy accessibility through computer or phone devices. Internet’s relative anonymity leads high risk groups to find it easier to meet sexual partners with similar characteristics through dating sites like Grindr, Jack’D, Adams4Adams etc. and mainstream social networking sites like Facebook, Twitter, or Instagram. According to various studies, young MSMs prefer to use dating sites and social networking sites more as a source to meet sexual partners than older MSMs.

Objective

To assess the usage of dating sites and social networking sites for finding sexual partners among newly diagnosed HIV positive MSMs in Harris County in 2014

In recent years, the use of social media has increased at an unprecedented rate. For example, the popular social media platform Reddit (http://www.reddit.com) had 83 billion page views from over 88,000 active sub-communities (subreddits) in 2015. Members of Reddit made over 73 million individual posts and over 725 million associated comments in the same year [1]. We use Reddit to track opium related discussions, because Reddit allows for throwaway and unidentifiable accounts that are suitable for stigmatized discussions that may not be appropriate for identifiable accounts. Reddit members exchange conversation via a forum like platform, and members who have achieved a certain status within the community are able to create new topically focused group called subreddits.

Objective

We aim to develop an automated method to track opium related discussions that are made in the social media platform called Reddit. As a first step towards this goal, we use a keyword-based approach to track how often Reddit members discuss opium related issues.

Social media messages are often short, informal, and ungrammatical. They frequently involve text, images, audio, or video, which makes the identification of useful information difficult. This complexity reduces the efficacy of standard information extraction techniques1. However, recent advances in NLP, especially methods tailored to social media2, have shown promise in improving real-time PH surveillance and emergency response3. Surveillance data derived from semantic analysis combined with traditional surveillance processes has potential to improve event detection and characterization. The CDC Office of Public Health Preparedness and Response (OPHPR), Division of Emergency Operations (DEO) and the Georgia Tech Research Institute have collaborated on the advancement of PH SA through development of new approaches in using semantic analysis for social media.

Objective

The objective of this analysis is to leverage recent advances in natural language processing (NLP) to develop new methods and system capabilities for processing social media (Twitter messages) for situational awareness (SA), syndromic surveillance (SS), and event-based surveillance (EBS). Specifically, we evaluated the use of human-in-the-loop semantic analysis to assist public health (PH) SA stakeholders in SS and EBS using massive amounts of publicly available social media data.