According to recent federal studies, e-cigarette use tripled among U.S. teens between 2013 and 2014. These figures suggest a significant change in public attitudes, but the social forces behind this shift remain difficult for public health officials and policymakers to assess. Though big data analytics have enhanced our ability to map how information spreads through social networks, we still lack a reliable means of modeling how millions of online interactions drive the diffusion of beliefs.
The ability to model broad-scale changes in belief could help policymakers target their efforts to educate the public about risky health behaviors.
The capacity to quickly gauge public attitudes could revolutionize the way policymakers forecast long-term health trends and respond to immediate crises. To develop a toolset capable of delivering these critical insights, researchers in the Social and Decision Analytics Laboratory (SDAL) have partnered with scientists from Sandia National Laboratories, Carnegie Mellon, and the Biocomplexity Institute’s Network Dynamics and Simulation Science Laboratory.
This project will build on our research team’s previous successes in computationally modeling interactions that produce lasting changes in belief. Our refined toolset will leverage the broad-scale analytical power of network science, which helps to identify the drivers of complex systems, and the psychological insight of cognitive science, which measures the mental processes that inform decision-making.
The development process will begin with a preliminary study into the spread of attitudes toward e-cigarettes. This project will employ a combination of crowd-sourcing, big data analytics, and simulated social networking platforms to create a controlled environment for online social experimentation and large-scale simulations.
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, natural disasters, and epidemics.
SDAL's research initiative will support the needs of both policymaking and higher education institutions.
This three-year development process will also provide valuable training opportunities for students and post-doctoral fellows in network science and computational psychology.