Systemic information gaps can take many forms: geographic or topical underrepresentation in user generated content, politically unfair content moderation, race or gender disparities in gig work 5-star reputation systems, or representing and recognizing diverse perspectives and needs in computational tools and models. Each type of systemic information gap differs, both in how it was created, and in how to fill the gap.
Our work in this space specifically focuses on information gaps in two primary settings: large-scale user generated content platforms, and crowd and gig work systems. We work to understand the systemic patterns of behavior that create information gaps, and the causal, contextual factors that influence individual behavior. We also build tools to help fill information gaps, focusing both on scalable approaches to information production, and on supporting individual needs and goals. We primarily focus on information gaps in the domains of human geography (socioeconomic status, demographic composition, urban-rural, etc.), though some of our work has focused on gender and political dimensions as well.
Faculty: Jacob Thebault-Speiker
Students: Zihan Gao, Yaxuan Yin
Click the tabs below to view our work on Information & Data Gaps
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Understanding Systemic Behavior That Create Information Gaps
Description
Publications
- Jacob Thebault-Spieker, Aaron Halfaker, Loren G. Terveen, and Brent Hecht 2018. Distance and Attraction: Gravity Models for Geographic Content Production. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), 148:1–148:13. https://doi.org/10.1145/3173574.3173722
- Jacob Thebault-Spieker, Aaron Halfaker, Loren G. Terveen, and Brent Hecht 2018. Distance and Attraction: Gravity Models for Geographic Content Production. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), 148:1–148:13. https://doi.org/10.1145/3173574.3173722
Individual Contextual Factors That Influence Behavior
- Molly G. Hickman, Viral Pasad, Harsh Kamalesh Sanghavi, Jacob Thebault-Spieker, and Sang Won Lee 2021. Understanding Wikipedia Practices Through Hindi, Urdu, and English Takes on an Evolving Regional Conflict. Proc. ACM Hum.-Comput. Interact. 5, CSCW1. https://doi.org/10.1145/3449108
Building Tools to Fill Information Gaps
- Jacob Thebault-Spieker, Sukrit Venkatagiri, Naomi Mine, and Kurt Luther 2023. Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23), 1280–1291. https://doi.org/10.1145/3593013.3594080
- Jacob Thebault-Spieker, Daniel Kluver, Maximilian A. Klein, Aaron Halfaker, Brent Hecht, Loren Terveen, and Joseph A. Konstan 2017. Simulation Experiments on (the Absence of) Ratings Bias in Reputation Systems. Proceedings of the ACM on Human-Computer Interaction 1, CSCW: 101:1–101:25. https://doi.org/10.1145/3134736