Crowds and Camera Traps: Genres in Online Citizen Science Projects

Abstract

Despite the importance of instruction for effective task completion in crowdsourcing, particularly for scientific work, little attention has been given to the design of instructional materials in crowdsourcing and citizen science. Consequences of inattention to tutorial design are further magnified by the diversity of citizen science volunteers. We use digital genre theory to identify the norms of tutorial design for the most abundant citizen science project type on the Zooniverse platform, camera trap image classification, where a highly-standardized task structure makes it a strong candidate as a specific genre of citizen science. Comparative content analysis of 14 projects’ features, tutorial design, and supporting materials identified a great deal of uniformity in some respects (indicating an emergent genre) but surprising variation in others. As further evidence of an emergent genre, the amount of mentoring the science team received and specific task features of the project appeared to impact tutorial design and supporting resources. Our findings suggest that genre theory provides a useful lens for understanding crowd science projects with otherwise disparate characteristics and identifying instances where the digital medium can be deployed more effectively for task instruction.