CRII: CHS: Mobile Crowdsourcing System for Public Safety

执法机构已经开始在他们的实践中利用众包应用和社交媒体，比如允许人们通过移动应用报告安全问题，以及在Twitter上报告犯罪。本研究旨在探讨在移动社交环境中激励和影响人们分享安全相关信息意愿的关键因素。该项目不仅专注于推进不同学科的知识，如众包系统、人机交互、社会计算和认知决策机制，还促进了对职业生涯早期科学家的指导和培训。这个项目将汇集来自计算机科学和心理学等不同学科的教职员工和学生研究人员。此外，将校园安全作为一个具体的应用领域，这项工作的重点是在地方校园安全机构和研究社区之间建立合作，这种合作可以保持和扩大，以促进一般公共安全。最后，该项目将创建一个可供所有高校安全机构采用的移动众包系统，以帮助提高校园安全。更广泛地说，移动系统可以成为社区警务的典范，以努力改善全国的公共安全。具体地说，这项研究将研究人们如何做出关于共享安全信息的决定，以及如何设计和实施可用于鼓励而不是强迫人们以知情和及时的方式共享此类信息的推动机制。利用认知心理学的计算建模技术，本项目将构建决策模型，以解释在这种背景下，哪些因素可能会影响人们的分享决策。内在因素，如改善社会的愿望或对知识的渴望，可能起作用，以及更多以社会/社区为基础的因素，如人们是否会受到伤害或财产损失。生成的模型将被集成到一个名为S4S(支持/安全共享)的校园安全移动众包应用程序中，该应用程序将用于评估推动、决策模型和系统的有效性。拟议研究任务的成功将产生：(A)就推动用户共享安全信息的机制的有效性和局限性提出设计建议和讨论；以及(B)设计和实施生产性移动众包系统，公共安全机构可利用该系统更快、更有效地收集有关社区安全的丰富信息。

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.