Two members of the Crowd Lab each had a paper accepted for presentation at the CSCW 2018 conference in Jersey City, NJ. The acceptance rate for this top-tier conference was 26%.
Ph.D. student Nai-Ching Wang presented “Exploring Trade-Offs Between Learning and Productivity in Crowdsourced History” with Virginia Tech professor of education David Hicks and Dr. Luther as co-authors. Here is the paper’s abstract:
Crowdsourcing more complex and creative tasks is seen as a desirable goal for both employers and workers, but these tasks traditionally require domain expertise. Employers can recruit only expert workers, but this approach does not scale well. Alternatively, employers can decompose complex tasks into simpler micro-tasks, but some domains, such as historical analysis, cannot be easily modularized in this way. A third approach is to train workers to learn the domain expertise. This approach offers clear benefits to workers, but is perceived as costly or infeasible for employers. In this paper, we explore the trade-offs between learning and productivity in training crowd workers to analyze historical documents. We compare CrowdSCIM, a novel approach that teaches historical thinking skills to crowd workers, with two crowd learning techniques from prior work and a baseline. Our evaluation (n=360) shows that CrowdSCIM allows workers to learn domain expertise while producing work of equal or higher quality versus other conditions, but efficiency is slightly lower.
Ph.D. student Tianyi Li presented “CrowdIA: Solving Mysteries with Crowdsourced Sensemaking” with Dr. Luther and Virginia Tech computer science professor Chris North as co-authors. Here is the paper’s abstract:
The increasing volume of text data is challenging the cognitive capabilities of expert analysts. Machine learning and crowdsourcing present new opportunities for large-scale sensemaking, but we must overcome the challenge of modeling the overall process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. In this paper, we explore how to crowdsource the sensemaking process via a pipeline of modularized steps connected by clearly defined inputs and outputs. Our pipeline restructures and partitions information into “context slices” for individual workers. We implemented CrowdIA, a software platform to enable unsupervised crowd sensemaking using our pipeline. With CrowdIA, crowds successfully solved two mysteries, and were one step away from solving the third. The crowd’s intermediate results revealed their reasoning process and provided evidence that justifies their conclusions. We suggest broader possibilities to optimize each component, as well as to evaluate and refine previous intermediate analyses to improve the final result.
Congratulations Nai-Ching and Tianyi!
