Social Monitoring for Public Health

Social Monitoring for Public Health

Michael J. Paul, Mark Dredze
ISBN: 9781681730950 | PDF ISBN: 9781681730967
Copyright © 2017 | 183 Pages | Publication Date: September, 2017

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Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible.

This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.

Table of Contents

Preface
Acknowledgments
A New Source of Big Data
Public Health: A Primer
Social Data
Methods of Monitoring
Public Health Applications
Limitations and Concerns
Looking Ahead
Bibliography
Authors' Biographies

About the Author(s)

Michael J. Paul, University of Colorado
Michael J. Paul is an assistant professor in Information Science at the University of Colorado, Boulder. He develops data science techniques for analyzing and organizing large text datasets, used for applications in health science and computational epidemiology. His work was among the first to identify the broad set of health issues that can be studied in Twitter, and his research has advanced the state of the art in influenza surveillance on multiple occasions. He is a former Twitter intern, and his work with Mark Dredze was featured in a video as part of the Twitter Stories series. He obtained his Ph.D. in Computer Science from Johns Hopkins University in 2015. See his website for more information: http://www.michaeljpaul.com

Mark Dredze, Johns Hopkins University
Mark Dredze is an associate professor in Computer Science at Johns Hopkins University. He is affiliated with the Center for Language and Speech Processing, the Human Language Technology Center of Excellence, and the Malone Center for Engineering in Healthcare. His work focuses on developing machine learning models for natural language processing (NLP) applications. He has pioneered new applications of these technologies in public health informatics, including work with social media data, biomedical articles, and clinical texts. His work is regularly covered by major media outlets, including NPR, The New York Times, and CNN. He obtained his Ph.D. in Computer Science from the University of Pennsylvania in 2009. See his website for more information: http://www.dredze.com

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