Comparing Generic and Community-Situated Crowdsourcing for Data Validation in the Context of Recovery from Substance Use Disorders
Association for Computing Machinery (ACM)
CHI ‘21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Targeting the right group of workers for crowdsourcing often achieves better quality results. One unique example of targeted crowdsourcing is seeking community-situated workers whose familiarity with the background and the norms of a particular group can help produce better outcome or accuracy. These community-situated crowd workers can be recruited in different ways from generic online crowdsourcing platforms or from online recovery communities. We evaluate three different approaches to recruit generic and community-situated crowd in terms of the time and the cost of recruitment, and the accuracy of task completion. We consider the context of Alcoholics Anonymous (AA), the largest peer support group for recovering alcoholics, and the task of identifying and validating AA meeting information. We discuss the benefits and trade-offs of recruiting paid vs. unpaid community-situated workers and provide implications for future research in the recovery context and relevant domains of HCI, and for the design of crowdsourcing ICT systems.
Rubya, Sabirat; Numainville, Joseph; and Yarosh, Svetlana, "Comparing Generic and Community-Situated Crowdsourcing for Data Validation in the Context of Recovery from Substance Use Disorders" (2021). Computer Science Faculty Research and Publications. 67.
ADA Accessible Version
Published as a part of the proceedings of the conference CHI ‘21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, No. 449 (May 2021): 1-17. DOI. © 2021 Association for Computing Machinery (ACM). Used with permission.
Sabirat Rubya was affiliated with University of Minnesota at the time of publication.