Assistant Professor
Management of Organizations | Sustainability
Leading researcher in computational sociology and online social network experiments
About
Douglas Guilbeault is an Assistant Professor in the Management of Organizations Group at the Haas School of Business. He studies how communication networks underlie the production and diffusion of cultural content, such as linguistic categories and social norms. This investigation extends to how communication dynamics are shaped by various sources of influence, such as organizational culture, political messaging, and the design of social media platforms. His work on these topics has appeared in a number of top journals, including Nature Communications, The Proceedings of the National Academy of the Sciences, and Management Science, as well as in popular news outlets, such as The Atlantic, Wired, and The Harvard Business Review. Guilbeault’s work has received top research awards from The International Conference on Computational Social Science, The Cognitive Science Society, and The International Communication Association. Guilbeault has also worked as a consultant on social media policy and people analytics for leading tech companies (e.g., Google and Facebook). His current research is funded by Facebook and the NIH.
Expertise and Research Interests
- Computational Sociology
- Collective Intelligence
- Culture and Cognition
- Social Contagion
- Categorization
- Social Media Policy
- Douglas Guilbeault and Damon Centola. Topological Measures for Identifying and Predicting the Spread of Complex Contagions. Nature Communications.
2021 - Douglas Guilbeault, Andrea Baronchelli, and Damon Centola. Experimental Evidence for Scale Induced Category Convergence across Populations. Nature Communications.
2021 - Damon Centola, Douglas Guilbeault, Urmimala Sarkar, Elaine Khoong, and Jingwen Zhang. The reduction of race and gender bias in clinical treatment recommendations using clinician peer networks in an experimental setting. Nature Communications.
2021 - Joshua Becker, Douglas Guilbeault, and Ned Smith. The Crowd Classification Problem: Social Dynamics of Binary Choice Accuracy. Management Science.
2021 - Joshua Becker, Douglas Guilbeault, Ned Smith. Some Questions Benefit from Group Discussion. Others Don’t.. Harvard Business Review.
2021 - Douglas Guilbeault, Joshua Becker, and Damon Centola. Social learning and partisan bias in the interpretation of climate trends. Proceedings of the National Academy of the Sciences.
2018 - Robert Gorwa and Douglas Guilbeault. Unpacking the Social Media Bot: A Typology to Guide Research and Policy. Policy and Internet.
2018
At Haas since 2020
2021 – present, Faculty Affiliate, Berkeley Institute for Data Science (BIDS)
2020 – present, Assistant Professor, Management of Organizations, Haas School of Business, University of California, Berkeley
Club 6. Recognition of Teaching Excellence for MBA Elective, “People Analytics.”
2020
Barbara and Gerson Bakar Faculty Fellowship
2020
Stanford’s Art of Science Award
2020
Changing Views in Data Science over Fifty Years
Facebook Content Moderation Policy Research Award
2019
Networked Crowdsourcing: An Online Experiment in Content Moderation. w. Damon Centola. ($100,000)
Computational Modeling Prize in Applied Cognition
2019
The Social Network Dynamics of Category Formation. The 41st Annual Meeting of the Cognitive Science Society. w. Andrea Baronchelli and Damon Centola. ($1000)
Best Paper Award
2018
Bipartisan Social Networks Reduce Political Bias in the Interpretation of Climate Trends. The International Conference on Computational Social Science. Kellogg, Northwestern, July. w. Joshua Becker and Damon Centola. ($1,000)
Dissertation Award
2018
The Network Dynamics of Interdisciplinary Research. The Institute for Research on Innovation and Science, The University of Michigan. ($15,000)
Top Student Paper Award
2018
What should we do about Political Automation? Challenges for Policy and Research. Communication & Technology Division, The International Communication Association, Prague, March. w. Robert Gorwa.
Network Effects on the Social Construction of Emotion in Organization
The Berkeley Culture Initiative
2022
Identifying Stereotypes in Online Images and their Implications for Algorithmic Bias
The Center for Equity, Gender, and Leadership (EGAL)
2022
Measuring Stereotypes in Online Images and their Impact on Recommendation Algorithms
Fisher Center for Business Analytics
2021
- Having Putin as a ‘common enemy’ won’t unite Americans., The Washington Post
- Want to go viral? Influencers won’t be much help if you’re trying to spread a complex idea, Fast Company, 07/22/2021
- Well-connected members of tight-knit groups spread controversial ideas much more readily than “influencers”, PNAS News, 07/30/2021
- Medical bias can be deadly. Our research found a way to curb it., The LA Times, 11/29/2021
- Why independent cultures think alike when it comes to categories: It’s not in the brain, phys.org, 01/12/2021
- How seeing a political logo can impair your understanding of facts, PBS NewsHour, 09/03/2018
- Regulating Bots on Social Media Is Easier Said Than Done, Slate, 08/09/2018
- How Political Campaigns Weaponize Social Media Bots, IEEE Spectrum, 10/18/2018
- Tinder nightmares: the promise and peril of political bots, Wired UK, 07/07/2017
- How Twitter Bots are Shaping the Election, The Atlantic, 11/01/2016