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The Community Data Science Collective (CDSC) is an interdisciplinary research group made up of faculty and students at the University of Washington Department of Communication, the Northwestern University Department of Communication Studies, the School of Information at UT Austin, the Brian Lamb School of Communication at Purdue University, and the University of Idaho Department of Psychology and Communication.
We are social scientists applying a range of quantitative and qualitative methods to the study of online communities. We seek to understand both how and why some attempts at collaborative production — like Wikipedia and Linux — build large volunteer communities and high-quality work products.
Our research is particularly focused on how the design of communication and information technologies shapes fundamental social outcomes with broad theoretical and practical implications — like an individual’s decision to join a community, contribute to a public good, or a group’s ability to make decisions democratically.
Our research is deeply interdisciplinary. It most frequently consists of “big data” quantitative analyses, but also includes qualitative analyses such as interview studies. Our work lies at the intersection of communication, sociology, human-computer interaction, and information science.
To learn more about the CDSC, please check out our about page (especially the links there). Prospective students should also review these materials.
Outreach
Our group is committed to public scholarship. We express this commitment through a series of events and publications directed at practitioner audiences and focused on translating our publications for nonacademic audiences.
- Community Data Science Blog — A blog that we update regularly with summaries of our papers, reflections on research and online communities, and whatever else is on our minds!
- Science of Community Dialogues — A series of events that bring together community leaders, organizers, and researchers to share evidence-based strategies for thriving communities.
- Research briefs — Short, accessible summaries of our research written for community leaders, practitioners, and policy makers.
Courses
In addition to research, we teach classes and run workshops. Some of that work is coordinated on this wiki. A more detailed list of workshops and teaching material on this wiki is on our Workshops and Classes page. On this page, we only list ongoing classes and workshops with their own wiki pages or syllabi.
University of Washington | Seattle Courses
- [Spring 2026] COM 597 B / CSSS 594 A: Text as Data — A MA/PhD seminar taught jointly with the Center for Statistics and the Social Sciences by Benjamin Mako Hill covering computational methods for analyzing text.
University of Washington | Bothell Courses
- [Spring 2026] CSS 490: Open Source Studio — Equips students to make a meaningful contribution to a software development project using open source software as a case study.
Research Resources
If you are a member of the collective, perhaps you're looking for CommunityData:Resources, which includes details on email, TeX templates, documentation on our computing resources, etc.
About This Wiki
This is open to the public and hackable by all but mostly contains information that will be useful to collective members, their collaborators, people enrolled in their projects, or people interested in building off of their work. If you're interested in making a change or creating content here, generally feel empowered to Be Bold. If things don't fit, somebody who watches this wiki will be in touch.
This is mostly a normal MediaWiki although there are a few things to know:
- There's a CAPTCHA enabled. If you create an account and then contact any collective member with your username (on or off wiki), they can turn the CAPTCHA off for you.
- Extension:Math is installed so you can write math here. Basically you just add math by putting TeX inside <math> tags like this: <math>\frac{\sigma}{\sqrt{n}}</math> and it will write $ {\frac {\sigma }{\sqrt {n}}} $.
Follow Us!
Follow us on Bluesky as @communitydata.science, @communitydata@social.coop in the Fediverse/Mastodon, or @comdatasci on X/Twitter.
Best of all, subscribe to the Community Data Science Collective blog to get email updates!
Research News
Recent posts from the blog:
- Troubleshooting in Computational Research Design: Report from a Workshop Series
- This winter quarter, a small group of CDSC students at the University of Washington participated in a series of workshops on Troubleshooting in Computational Research Design. The workshops were organized by Yibin Fan. Research articles typically present a streamlined account of research design. How should a concept be operationalized? What counts as valid data? The …
Continue reading "Troubleshooting in Computational Research Design: Report from a Workshop Series"
- — rantang 2026-04-18
- CDSC at CHI 2026!
- Come hang out with us at CHI 2026 in Barcelona April 13-17! Members of the Community Date Science Collective will be presenting work and we’d love to see you there. We’ll be in a few places: CDSC alumnus and current postdoctoral fellow Sohyeon Hwang will be presenting Governing Together: Toward Infrastructure for Community-Run Social Media. This …
- — madisondeyo 2026-04-08
- When AI Feels Like a Confidant: The Illusion of Shared Privacy in AI Companions
- AI companions like Replika and Character.AI are increasingly experienced not as tools, but as relational partners. They remember conversations, express empathy, and respond with emotional continuity. For many users, talking to an AI feels closer to confiding in someone than interacting with software. But what happens to privacy when a system feels like a relationship? …
Continue reading "When AI Feels Like a Confidant: The Illusion of Shared Privacy in AI Companions"
- — chsuenchi 2026-03-09
- AI Didn’t Start the Fire: How Stack Exchange Moderators and Users Demonstrate Exit, Voice, and Loyalty
- Generative AI technologies rely on content from knowledge communities as their training data. However, these communities receive little in return and instead experience increasing moderation burdens imposed by an influx of AI-generated content. Moreover, as platform operators sell their content to AI developers whose products may substitute for their work, these communities see a decrease …
- — yiweiwu 2026-02-24
- Why do people participate in similar online communities?
- Note: We have missed publishing blog posts about academic papers over the past few years. To ensure that my blog contains a more comprehensive record of our published papers and to surface these for folks who missed them, I will be periodically publishing blog posts about some “older” published projects. It seems natural to think …
Continue reading "Why do people participate in similar online communities?"
- — Benjamin Mako Hill http://mako.cc 2026-02-16
