A survey of social media data analysis for physical activity surveillance

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Highlights

  • Social data has the potential to provide real-time physical activity surveillance.

  • We described current social data analysis methods for predicting physical activity.

  • These methods include topic modeling, sentiment and network analysis.

  • Recommendations to help further this field of research were provided in the paper.

Abstract

Social media data can provide valuable information regarding people's behaviors and health outcomes. Previous studies have shown that social media data can be extracted to monitor and predict infectious disease outbreaks. These same approaches can be applied to other fields including physical activity research and forensic science. Social media data have the potential to provide real-time monitoring and prediction of physical activity level in a given region. This tool can be valuable to public health organizations as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (e.g. surveys, observational studies). As a result, this tool could help public health organizations better mobilize and target physical activity interventions. The first part of this paper aims to describe current approaches (e.g. topic modeling, sentiment analysis and social network analysis) that could be used to analyze social media data to provide real-time monitoring of physical activity level. The second aim of this paper was to discuss ways to apply social media analysis to other fields such as forensic sciences and provide recommendations to further social media research.

Introduction

Social media technology, such as Twitter, allows users to communicate with each other by sharing short messages and website links. Users often share their thoughts, feelings and opinions on these social media platforms and as a result, social media data could be used to provide real-time monitoring of psychological and behavior outcomes that inform levels of physical activity.1, 2 A unique aspect about social media data from Twitter is that the posts are public and geo-tagged and thus, all Internet users, including health researchers, can readily access this data. Twitter usage has increased 30% from 2012 to 2014.3 Currently 1 in 4 adults uses Twitter and usage is expected to increase steadily in the future.3 Due to the rapid growth in social media use, these sites provide an enormous amount of data (e.g., over 500 million tweets per day on Twitter). The growing body of social media data is becoming a central part of big data research as these data can be modeled alongside other datasets (e.g. biomedical, crime rate) and used to predict outcomes from these datasets. Research has already shown that data from social media technologies can be used for novel approaches to identifying infections disease outbreaks such as influenza transmission4 and HIV outbreaks.5 Methods used to analyze social media data for predicting infectious disease outbreaks could be applied to physical activity research and other fields of study such as forensic science. Currently, no studies have described the application for using social media data to predict and monitor physical activity levels. Therefore, the first part of this paper was to describe current social media analysis approaches (e.g. topic modeling, sentiment analysis and social network analysis) that can be used to monitor and predict levels physical activity in real-time. The second part of this paper aim to discuss ways to apply social media analysis to other fields such as forensic sciences and provide recommendations to further social media research.

Section snippets

Part 1) methods of analyzing social media data for physical activity surveillance

Regular physical activity is associated with important health benefits and it is critical to chronic disease prevention and management.6, 7 Currently, only about 20% adults in the United States meet the recommended amount of physical activity (at least 150 min of moderate-intensity aerobic activity per week8). Based on the latest physical activity survey from Center for Disease Control and Prevention (CDC), the prevalence of physical inactivity varies across the United States.9 The lack of

Part 2) recommendations to advance the field of social media research

Social media-based data have the potential to not only be used as a tool for physical activity surveillance and monitoring, but also be applied to fields such as forensic science. For example, using topic modeling, geo-tagged tweets, and sentiment analysis, social media data may be used to build tools to monitor and predict crime rates, and drug trafficking activates with in a region. Analyzing social media data may help identify individuals who are victims of violence. This may be done by

Conclusion

Surveillance and monitoring of changes in people's behavior and outcomes can help inform government agencies and organizations mobilize appropriate interventions. Social media data has the potential to provide real-time surveillance and prediction of physical activity levels. Current social media analyses approaches that could be used to monitor and predict physical activity levels in real-time include topic modeling, sentiment analysis and network analysis. These approaches can also be applied

Conflicts of interest

None.

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