Driven by Curiosity: Interaction Techniques and Incentive Mechanisms for Crowdsourcing Scientific Tasks

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Science is increasingly data-intensive; yet, many research tasks involving the collection, annotation and analysis of data are not yet fully automated by computers. The idea of citizen science is to engage massive number of people over the Web to contribute and process scientific data, to tackle problems where the scale, spread and complexity of the data is beyond the analytic capability of algorithms or the bandwidth of small teams.  The success of these citizen science platforms is in part due to an existing population of hobbyists (e.g., birders, amateur astronomers) who are already interested and eager to help. Unfortunately, the reverse scenario is actually more common - often scientists have research data and problems that are unfamiliar to most people, and struggle to transform labor intensive data processing tasks into something that is intrinsically motivating for participants to do. The lack of mechanisms for incentivizing and sustaining participation presents a major roadblock preventing citizen science systems from widespread adoption.***In this proposed research, we will tackle the issue of engagement in citizen science by investigating curiosity as a driver for participation. In particular, we will develop new interaction techniques and incentive mechanisms to support "curiosity-inducing designs" based on a well-established psychological model of curiosity - Loewenstein's information gap theory - which says that people are curious when they are made aware of the gap in their knowledge, and engage in exploratory, information-seeking behavior in order to close this information gap and resolve uncertainty.  Our research is guided by three objectives.  Objective 1 aims to operationalize the concept of curiosity into interfaces based on information gap theory, and study how these curiosity-inducing interfaces affect the way citizen and professional scientists interact with citizen science systems. Objective 2 is to characterize how these interventions affect different types of crowds, e.g., non-expert vs expert volunteers, K-12 and college students, and scientists. Objective 3 is to improve the generalizability of curiosity-inducing interfaces, making them applicable to many kinds of citizen science projects and extending their capabilities to not only induce, but sustain, curiosity. This research will be conducted using a testbed called Curio, a general purpose citizen science platform that enables researchers from diverse fields, who are domain experts but not necessarily technically savvy or familiar with crowdsourcing, to create and manage scientific crowdsourcing projects.  Beyond citizen science, the expected outcome of this research is a novel, well-characterized, thoroughly evaluated design framework for motivating participation that is broadly applicable to volunteer-based crowdsourcing. **