Best Poster/Demo Award at HCOMP 2019

HCOMP 2019 Poster/Demo chairs (left) and Dr. Kurt Luther (center), Sukrit Venkatagiri (second from right), and David Mitchell (right).

The Crowd Lab won the Best Poster/Demo Award at HCOMP 2019! Congratulations to Crowd Lab postdoc and team leader Jacob Thebault-Spieker, Ph.D. student Sukrit Venkatagiri, and undergraduate researchers David Mitchell and Chris Hurt. Their poster/demo was titled, “PairWise: Mitigating political bias in crowdsourced content moderation.”

The Crowd Lab also won the Best Poster/Demo Award at HCOMP 2018.

Article published in Human Computation journal

Our article, Read-Agree-Predict: A crowdsourced approach to discovering relevant primary sources for historians, was published in Human Computation Journal. Congratulations to Crowd Lab Ph.D. alumnus Dr. Nai-Ching Wang, who led the paper, along with co-authors Dr. David Hicks (Education) and Dr. Paul Quigley (History). The abstract for the article is:

Historians spend significant time looking for relevant, high-quality primary sources in digitized archives and through web searches. One reason this task is time-consuming is that historians’ research interests are often highly abstract and specialized. These topics are unlikely to be manually indexed and are difficult to identify with automated text analysis techniques. In this article, we investigate the potential of a new crowdsourcing model in which the historian delegates to a novice crowd the task of labeling the relevance of primary sources with respect to her unique research interests. The model employs a novel crowd workflow, Read-Agree-Predict (RAP), that allows novice crowd workers to label relevance as well as expert historians. As a useful byproduct, RAP also reveals and prioritizes crowd confusions as targeted learning opportunities. We demonstrate the value of our model with two experiments with paid crowd workers (n=170), with the future goal of extending our work to classroom students and public history interventions. We also discuss broader implications for historical research and education.