A computational cognitive modeling approach to understand and design mobile crowdsourcing for campus safety reporting. Issue 102 (June 2017) - Huang, Yun; White, Corey; Xia, Huichuan; Wang, Yang
Abstract: The under-reporting of public safety incidents is a long-standing issue. In this paper, we propose a computational cognitive modeling approach to understand and design a mobile crowdsourcing system for improving campus safety reporting. In particular, we adopt drift-diffusion models (DDMs) from cognitive psychology to investigate the effect of various factors on users' reporting tendency for public safety. Our lab experiment and online study show consistent results on how location context impacts people's reporting decisions. This finding informs the design of a novel location-based nudge mechanism, which is tested in another lab experiment with 84 participants and proved to be effective in changing users' reporting decisions. Our follow-up interview study further suggests that the influence of people's mobility patterns (e.g., expected walking distance) could explain why the nudge design is effective. Our work not only informs the design of mobile crowdsourcing for public safety reporting but also demonstrates the value of applying a computational cognitive modeling approach to address HCI research questions more broadly. Highlights: A computational cognitive modeling approach is used to study crowdsourcing decisions. Location context and crime severity impact people's safety reporting decisions. A novel location-based nudge mechanism is created and proved to be effective.