The project at the Virginia Bioinformatics Institute will explore how interactions on Twitter influence attitudes toward e-cigarettes using a combination of online social experiments, publicly available data, and simulated Twitter networks.
E-cigarette use tripled among U.S. teens between 2013 and 2014 according to a recent survey by the Centers for Disease Control and Prevention.
“Our group is creating a set of tools to model the microstructure of social influence on a massive, network scale,” said Mark Orr, the project’s principal investigator and a research associate professor in the Virginia Bioinformatics Institute’s Social Decision and Analytics Laboratory. “The ultimate goal is to help policymakers reliably predict how thousands of individual interactions aggregate into general social attitudes.”
The project will apply new methods developed by Orr and his collaborators to simulate interactions in social systems that produce rapid, lasting changes in attitudes related to health behavior and computationally model the spread of beliefs within a population.
With the new grant, the team plans to refine these tools and bridge the gap between network science, which provides insights about the most effective ways to intervene in large, complex networks, and cognitive science, which quantifies the mental processes that inform individuals’ decisions, like memory and attitude formation.
“This project is interdisciplinary to its core, an extension of several longstanding research collaborations,” said Samarth Swarup, co-investigator and research assistant professor in VBI’s Network Dynamics and Simulation Science Laboratory. “The team combines expertise in computer science, psychology, public health, and sociology.”
The four-year study will be conducted in partnership with co-investigators Kiran Lakkaraju of Sandia National Laboratories, and David Plaut of Carnegie Mellon University’s Center for the Neural Basis of Cognition.
The group’s findings may be applied to analyze a variety of situations where the diffusion of information across social networks impacts public health decision-making, including instances of cyberterrorism, disease epidemics, and natural disasters.