Social Media

Obesity can lead to the death of at least 2.8 million people each year1, yet the rate of obesity around the world has continuously increased over the past 30 years1. Societal changes, including increased food consumption and decreased physical activity, have been determined as two of the main drivers behind the current obesity pandemic2. Examining socio-cultural factors (i.e., attitudes or perceptions of cultural groups)3 associated with food consumption and weight loss can provide important insights to guide effective interventions and a novel surveillance approach to characterize population obesity trends from sociological perspectives. The primary goal of this study is to examine socio-cultural factors associated with food consumption and weight loss by conducting sentiment analysis on related online chatters. The secondary goal is to discuss the potential implications of being exposed to these different chatters in the online environment. Scientific evidence in support of using social media to understand socio-cultural factors and its potential implications can be illustrated in two concise assertions. First, online chatters, including discussions on social media, have been shown to be an effective data source for understanding public interests4,5. Second, prolonged participation in social media has been suggested to have impacts on users6-8.

Objective: We aim to better understand socio-cultural factors (i.e., attitudes or perceptions of cultural groups) associated with food consumption and weight loss via sentiment analysis on tweets, short messages from Twitter.

Overweight and obesity are recognized as one of the greatest modern public health problems1, yet worldwide prevalence of obesity has nearly doubled over the past 30 years2. As part of a strategy to control the obesity pandemic, the WHO recommends an obesity surveillance at the population level3. Empirical studies have shown the importance of social networks in obesity4 and new strategies focusing on social interactions and environments have been proposed5 to prevent the further increase in obesity prevalence. With the increasing use of the internet, online social networks, interactions, and environments (i.e., online social relational factors) deserve more attention. Nearly three- quarters of Americans go online daily6, for functions like connecting with individuals via social network sites7. Like face to face interactions, studies have suggested that social interactions and networks on the internet can influence behavior changes8. Previous studies examining social networking sites typically examine a few selected social networking sites (example studies9,10), although individuals could be members of multiple social networking sites. To better leverage online social relational factors for the purpose of characterizing and monitoring population obesity trends, we investigate weight management community members' other communities and their level of participation, a first step toward utilizing online multifactorial social interactions and environments.

Objective: We aim to better understand online social interactions and environments of individuals interested in weight management from a social media platform called Reddit.

There is a significant body of literature on the use of social media for monitoring ailments such as influenza-like illness1 and cholera,2 as well as public opinions on topics such as vaccination.3 In general, these studies have shown that social media correlates well with official data sources,1,2,3 with the trends identifiable before official data are available.2 However, less is known about the impact of integrating social media into public health practice, and resulting interventions. Therefore, the ISDS Social Media for Disease Surveillance Workgroup initiated a systematic literature review on the use of social media for actionable biosurveillance.

Objective

The objective of this study is to systematically review the literature on the use of social media for biosurveillance in order to evaluate whether this data source can improve public health practice or community health outcomes.

The spread of infectious diseases is facilitated by human travel. Infectious diseases are often introduced into a population by travelers and then spread among susceptible individuals. Likewise uninfected susceptible travelers can move into populations sustaining the spread of an infectious disease.

Several disease-modeling efforts have incorporated travel data (e.g., air, train, or subway traffic) as well as census data, all in an effort to better understand the spread of infectious diseases. Unfortunately, most travel data is not fine grained enough to capture individual movements over long periods and large spaces. It does not, for example, document what happens when people get off a train or airplane. Thus, other methods have been suggested to measure how people move, including both the tracking of currency and movement of individuals using cell phone data. Although these data are finer grained, they have their own limitations (e.g., sparseness) and are not generally available for research purposes.

FourSquare is a social media application that permits users to "check-in" (i.e., record their current location at stores, restaurants, etc.) via their mobile telephones in exchange for incentives (e.g., location-specific coupons). FourSquare and similar applications (Gowalla, Yelp, etc.) generally broadcast each check-in via Twitter or Facebook; in addition, some GPS-enabled mobile Twitter clients add explicit geocodes to individual tweets.

Here, we propose the use of geocoded social media data as a real-time fine-grained proxy for human travel.

Objective

To use sequential, geocoded social media data as a proxy for human movement to support both disease surveillance and disease modeling efforts.

A devastating cholera outbreak began in Haiti in 2010. Sequencing of Vibrio cholerae isolates showed that the epidemic was likely the result of the introduction of cholera from a distant geographic source. The same strain of cholera was detected in other countries within 100 days. The unique instigation and geographic spread of this epidemic highlight the need for improvements in timely global outbreak surveillance. Novel information sources have been shown to provide early information about public health events and disease epidemiology. Particularly, volume of Internet metrics such as web searches or micro-blogs have been shown to be a good corollary for public health events. In this study, we evaluate geographic trends in online social media following an infectious disease outbreak to determine whether this may enable prediction of secondary outbreak locations.

Objective

To evaluate the association between and develop a risk model relating geographic trends of social media and spread of an infectious disease outbreak.

Public health surveillance relies on multiple systems and methodologies for data collection, analysis, and interpretation. Each component provides only part of the picture, such as detection of possible outbreaks or events of concern; geographic profiles or time courses of disease activity; or indicators of clinical severity by age, risk factors, etc. Novel, unstructured data sources like Twitter feeds and aggregated news reports are growing as a source of information about health and disease. What and where are the contributions of these nontraditional, often non-specific, data types to BSV? The answer will depend on the purpose and target population. Different data streams often have greater utility for one BSV function (e.g., outbreak detection) than another (e.g., situation awareness). Furthermore, public health agencies at different levels need and use data differently, as determined by their priorities for public health. New types of data can also be useful for disease prediction and forecasting, pandemic modeling, and developing analytic tools. Before any new data modality can be integrated into standards of surveillance practice, it needs to be evaluated for its contribution to understanding disease activity and the value added when compared to other sources of data with regard to validity, timeliness, accuracy, representativeness, and positive and negative predictive values. Furthermore, questions remain about when novel, unstructured, or nontraditional data sources are acceptable evidence to inform decision-making and public health actions. To address this, the strengths and weaknesses of different types of data for various surveillance functions need to be discussed among stakeholders that bring various perspectives from surveillance research, practice, and policy.

Objective

To gather thought leaders in informatics, public health practice, surveillance research, and strategic decision-making to provide their insights into where and how to effectively integrate novel data streams, such as social media, into biosurveillance (BSV) systems and standards of public health surveillance practice.

Disease outbreak detection based on traditional surveillance datasets, such as disease cases reported from hospitals, is potentially limited in that the collection of clinic information is costly and time consuming. However, social media provides the vast amount of data available in real time on the internet at almost no cost. Our solution, NPHGS, provides a nonparametric statistical approach for outbreak detection that well addresses the key technical challenges in social media streams.

Objective

We present a new method for disease outbreak detection, the 'Non-Parametric Heterogeneous Graph Scan (NPHGS)'. NPHGS enables fast and accurate detection of emerging space-time clusters using Twitter and other social media streams where standard parametric model assumptions are incorrect.

Much attention has been given recently to the purported ability of social media to provide early warning and/or situational awareness and event characterization during a biological event of national concern. The National Biosurveillance Integration Center's (NBIC) innovation project on Social Media Analysis seeks to demonstrate the viability of extracting relevant, health information from social media data, with the ultimate goal to establish an operational social media system for biological event surveillance. Early work in this project has focused on demonstrating the relevance of social media to the biosurveillance problem through data analysis and algorithm development. Preliminary assessments of a commercial social media product also yielded valuable insights for the system architecture required to support such an operational tool. In addition to continued analysis of data utility (algorithm development) and system architecture, future work will include development of a comprehensive concept of operations (CONOPS) for implementation and use of a social media capability within the NBIC.

Objective

Through ongoing and future projects we will examine the utility of social media data for biosurveillance, including machine learning approaches for algorithm development, as well as the system and organizational architectures required to implement an operational system.