The goal of this project is to use peer-driven recruitment to sample from and send information to hidden, underrepresented, or underground online communities. Specifically, the project will examine how social media can be used to extract information from and distribute information to hidden communities, determine whether the framework to be developed can provide new insights into the behavior of community members, advance the science of peer-to-peer sampling techniques, and determine whether trust in social media affects the peer-to-peer recruitment process.

Intellectual Merit This project will serve to answer the following questions. How can we best apply computational resources that are associated with online social media to improve processes for collecting information from and distributing information to hidden populations? To what degree do the known deficiencies in state-of-the-art bias estimators impact the effectiveness of peer-to-peer-based sampling and how well can these deficiencies be corrected by new estimators and/or sampling design? To what extent do individuals invest trust in various social media (particularly Facebook and email) and how much does degree-of-trust affect peer-to-peer recruitment processes, especially with respect to identification with a hidden or marginalized group? These are important questions, and answers to them will serve to advance the both the social and computational sciences, and their synergistic combinations.

Broader Impacts This project will provide an open source system for remotely interacting with online communities, particularly but not limited to hidden communities. The project will have a substantial education component in that the proposed system will have a simulation mode that will enable it to be used for class projects. By providing a way to address problems that impact underrepresented groups, it will also provide meaningful research and educational opportunities in computing to members of those groups. In addition, it will establish an interdisciplinary, intercollegiate laboratory in online social network research that will institutionalize preexisting working relationships and provide a home for an ongoing seminar series in network science.

National Science Foundation (NSF)
Division of Social and Economic Sciences (SES)
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Frederick M Kronz
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Rochester Institute of Tech
United States
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