CRII: CHS: Mobile Crowdsourcing System for Public Safety

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Law enforcement agencies have begun to tap into crowdsourcing applications and social media in their practices, such as allowing people to report safety issues via mobile apps and reporting crimes on Twitter. This research aims to investigate key factors that motivate and influence people's willingness to share safety-related information in a mobile social environment. This project not only focuses on advancing knowledge in different disciplines such as crowdsourcing systems, human-computer interaction, social computing, and cognitive decision mechanisms, but also promotes mentoring and training of early-career scientists. This project will bring together faculty and student researchers from different disciplines such as computer science and psychology. Further, using campus safety as a concrete application domain, this work focuses on establishing collaboration between local campus safety agencies and the research community, which can be maintained and expanded to promote public safety in general. Finally, the project will create a mobile crowdsourcing system that can be adopted by all safety agencies of institutions of higher education to help improve campus safety. More broadly, the mobile system can become a model for community policing in an effort to improve public safety nationwide. Specifically, the research will examine how people make decisions regarding sharing safety information as well as design and implement nudge mechanisms that can be used to encourage rather than force people towards sharing such information in an informed and timely manner. Using computational modeling techniques from cognitive psychology, this project will build decision models to explain what factors may impact people's sharing decision-making in this context. Intrinsic factors such as the desire to improve society or the desire for knowledge may play a role, as well as more social/community based factors such as whether people will be hurt or property damaged. The resulting models will be integrated into a mobile crowdsourcing app for campus safety, called S4S (Sharing for Support/Safety), which will be deployed to evaluate the nudges, decision models, and the effectiveness of the system. Success of the proposed research tasks will yield: (a) design suggestions and discussions on the effectiveness and limitations of mechanisms that nudge users to share safety information; and (b) the design and implementation of a production mobile crowdsourcing system, which can be leveraged by public safety agencies to collect rich information on community safety more promptly and efficiently.