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NORC at the University Chicago

Sherry Emery

Director
Social Data Collaboratory
Senior Fellow
Public Health
Sherry is a leading researcher on the effects of social media on tobacco and other substance use.

Sherry is the director of NORC’s Social Data Collaboratory. Her interdisciplinary research applies the approaches of health communication, data science, and public policy to understand how both traditional and new media influence health behavior. For over a two decades, she has focused on the roles that tobacco control and other tobacco-related advertising play in shaping attitudes, beliefs, and tobacco-use behaviors among youth and adults. Since 2010, her research scope has expanded to include examinations of the impact of numerous public health media campaigns, including illicit drug use prevention, obesity prevention, mental health promotion, and vaccine promotion campaigns, as well how pharmaceutical advertising affects health care utilization.

Sherry serves or has served as the principal investigator on research studies funded by the National Cancer Institute; National Heart, Lung, and Blood Institute; the Centers for Disease Control and Prevention; the Robert Wood Johnson Foundation; the State of California; and the Truth Initiative.

Before joining NORC with her team in July 2016, Sherry was a senior research scientist at the Institute for Health Research and Policy (IHRP), where she established and directed the Health Media Collaboratory (now NORC’s Social Data Collaboratory) in 2011. She was an IHRP Fellow and a Research Professor at the University of Illinois Chicago School of Public Health.

Project Contributions

Social Media’s Influence on Flavored Tobacco Use

Investigation of youth-targeted flavored tobacco promotion on social media to inform regulatory decisions

Client:

National Institute on Drug Abuse; Food & Drug Administration

Health Communication AI

Combining the trust-building ability of digital opinion leaders with the scalability of AI to revolutionize health communication

Developing a Supervised Model of Online COVID Vaccine Information

Examining AI biases in model development to accurately detect and classify online COVID-19 vaccine information

Client:

Amazon Web Services (AWS)

Publications

Research Library & Findings