Supporting a Nation of Neighbors with Community Analysis Visualization Environments (SOCS)

Computationally-mediated civic participation is emerging as a solution to contemporary problems associated with economic and social issues such as healthcare, energy sustainability, education, environmental protection, and disaster response. The NSF-funded research project conducted by Ben Shneiderman, Alan Neustadtl, and Catherine Plaisant at the University of Maryland will study reasons for successes and failures of the community safety system, Nation of Neighbors. The results will enable interventions to shift the balance towards increasing success. One product of the research will be a computer-based Community Analysis Visualization Environment (CAVE) that will enable community managers to use a visual analytic toolkit to take the pulse of their communities by identifying effective and ineffective components of the community participation program, and will enable researchers to compare large numbers of communities to understand the features that distinguish successful from failing community participation programs. The project will test the four-stage Reader-to-Leader Framework -- which assumes that participation moves from reader to contributor to collaborator to leader, with fewer and fewer participants moving into each subsequent stage -- by studying community manager strategies for coping with the practical challenge of increased participation as well as threatening disruptions caused by external events, malicious attacks, harmful rumors, and disaffected members.

In addition the results will have general implications for many computationally-mediated civic participation systems such as those designed for coping with natural disasters (earthquakes, toxic waste discharges, etc.), medical outbreaks (food poisoning, flu, pandemics, etc.), and human threats (terrorists, serial killers, bombers, arsonists, etc.). The computational tools developed for the project will also be useful to researchers studying community participation networks. The research may also provide useful insights into the working of other types of social networks and might have implications for organizations where information is shared by large numbers of people, such as hospitals and school districts.

Principal Investigators

Alan Neustadtl