About

Aaron Shaw is an Associate Professor of Communication Studies and Sociology (by courtesy) at Northwestern University. He studies collaboration, governance, and inclusion in participatory organizations, with a focus on online communities that create public information resources such as Wikipedia. Shaw is affiliated with the Center for Human-Computer Interaction + Design, the Institute for Policy Research, and the Buffett Institute for Global Affairs at Northwestern. He is also a Faculty Associate of the Berkman-Klein Center for Internet & Society at Harvard University and is a co-founder of the Community Data Science Collective. Shaw has held a fellowship at the Center for Advanced Study in the Behavioral Sciences at Stanford University and served as Scholar in Residence in King County, Washington during the 2022-2023 academic year. He holds degrees from Stanford University and the University of California, Berkeley, including a PhD in Sociology. His research has received awards from scholarly associations such as the American Sociological Association, the Association for Computing Machinery, and the American Political Science Association.

Research topics

• Computer Science
• World Wide Web
• Social Science
• Knowledge management
• Political Science
• Engineering
• Sociology
• Microeconomics
• Geography
• Public relations
• Economy
• Economics
• Environmental health
• Psychology
• Social psychology
• Demographic economics
• Applied psychology
• Linguistics
• Medicine
• Business

Selected publications

• This human study did not involve human subjects: Validating LLM simulations as behavioral evidence

  arXiv (Cornell University) · 2026-02-17

  articleOpen accessSenior author

  A growing literature uses large language models (LLMs) as synthetic participants to generate cost-effective and nearly instantaneous responses in social science experiments. However, there is limited guidance on when such simulations support valid inference about human behavior. We contrast two strategies for obtaining valid estimates of causal effects and clarify the assumptions under which each is suitable for exploratory versus confirmatory research. Heuristic approaches seek to establish that simulated and observed human behavior are interchangeable through prompt engineering, model fine-tuning, and other repair strategies designed to reduce LLM-induced inaccuracies. While useful for many exploratory tasks, heuristic approaches lack the formal statistical guarantees typically required for confirmatory research. In contrast, statistical calibration combines auxiliary human data with statistical adjustments to account for discrepancies between observed and simulated responses. Under explicit assumptions, statistical calibration preserves validity and provides more precise estimates of causal effects at lower cost than experiments that rely solely on human participants. Yet the potential of both approaches depends on how well LLMs approximate the relevant populations. We consider what opportunities are overlooked when researchers focus myopically on substituting LLMs for human participants in a study.

  PublisherOA PDF

• SMART: A Social Movement Analysis & Reasoning Tool with Case Studies on #MeToo and #BlackLivesMatter

  Open MIND · 2026-01-28

  preprint

  Social movements supporting the UN's Sustainable Development Goals (SDGs) play a vital role in improving human lives. If journalists were aware of the relationship between social movements and external events, they could provide more precise, time-sensitive reporting about movement issues and SDGs. Our SMART system achieves this goal by collecting data from multiple sources, extracting emotions on various themes, and then using a transformer-based forecasting engine (DEEP) to predict quantity and intensity of emotions in future posts. This paper demonstrates SMART's Retrospective capabilities required by journalists via case studies analyzing social media discussions of the #MeToo and #BlackLivesMatter before and after the 2024 U.S. election. We create a novel 1-year dataset which we will release upon publication. It contains over 2.7M Reddit posts and over 1M news articles. We show that SMART enables early detection of discourse shifts around key political events, providing journalists with actionable insights to inform editorial planning. SMART was developed through multiple interactions with a panel of over 20 journalists from a variety of news organizations over a 2-year period, including an author of this paper.

  DOI

• Algorithmic News Content Personalization and Readers’ Attitudes

  Digital Journalism · 2026-02-25 · 1 citations

  article
  PublisherDOI

• DEEP: A Discourse Evolution Engine for Predictions About Social Movements

  Proceedings of the AAAI Conference on Artificial Intelligence · 2026-03-14

  articleOpen access

  Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025, which is publicly available for research purposes.

  PublisherOA PDFDOI

• This human study did not involve human subjects: Validating LLM simulations as behavioral evidence

  arXiv (Cornell University) · 2026-02-17

  preprintOpen accessSenior author

  A growing literature uses large language models (LLMs) as synthetic participants to generate cost-effective and nearly instantaneous responses in social science experiments. However, there is limited guidance on when such simulations support valid inference about human behavior. We contrast two strategies for obtaining valid estimates of causal effects and clarify the assumptions under which each is suitable for exploratory versus confirmatory research. Heuristic approaches seek to establish that simulated and observed human behavior are interchangeable through prompt engineering, model fine-tuning, and other repair strategies designed to reduce LLM-induced inaccuracies. While useful for many exploratory tasks, heuristic approaches lack the formal statistical guarantees typically required for confirmatory research. In contrast, statistical calibration combines auxiliary human data with statistical adjustments to account for discrepancies between observed and simulated responses. Under explicit assumptions, statistical calibration preserves validity and provides more precise estimates of causal effects at lower cost than experiments that rely solely on human participants. Yet the potential of both approaches depends on how well LLMs approximate the relevant populations. We consider what opportunities are overlooked when researchers focus myopically on substituting LLMs for human participants in a study.

  PublisherDOI

• SMART: A Social Movement Analysis & Reasoning Tool with Case Studies on #MeToo and #BlackLivesMatter

  ArXiv.org · 2026-01-28

  articleOpen access

  Social movements supporting the UN's Sustainable Development Goals (SDGs) play a vital role in improving human lives. If journalists were aware of the relationship between social movements and external events, they could provide more precise, time-sensitive reporting about movement issues and SDGs. Our SMART system achieves this goal by collecting data from multiple sources, extracting emotions on various themes, and then using a transformer-based forecasting engine (DEEP) to predict quantity and intensity of emotions in future posts. This paper demonstrates SMART's Retrospective capabilities required by journalists via case studies analyzing social media discussions of the #MeToo and #BlackLivesMatter before and after the 2024 U.S. election. We create a novel 1-year dataset which we will release upon publication. It contains over 2.7M Reddit posts and over 1M news articles. We show that SMART enables early detection of discourse shifts around key political events, providing journalists with actionable insights to inform editorial planning. SMART was developed through multiple interactions with a panel of over 20 journalists from a variety of news organizations over a 2-year period, including an author of this paper.

  PublisherOA PDF

• In Science Journals

  Science · 2026-03-12

  article

  Highlights from the Science family of journals

  PublisherDOI

• Replication materials for "Communication and Collaboration in Stewarded Open Source Software Development"

  Harvard Dataverse · 2026-04-23

  datasetOpen access

  Replication materials for the DIIF report "Communication and Collaboration in Stewarded Open Source Software Development"

  PublisherDOI

• Governing Together: Toward Infrastructure for Community-Run Social Media

  2026-04-13 · 1 citations

  articleOpen access

  Decentralizing the governance of social computing systems to communities promises to empower them to make independent decisions, with nuance and in context. Yet, communities do not govern in isolation. Many problems communities face are common, or move across their boundaries. We propose designing for inter-community governance: mechanisms that support relationships between communities toward coordinating on governance issues. Drawing from workshops with 24 individuals on decentralized, community-run social media, we present six challenges in designing for inter-community governance surfaced through ideas discussed in workshops. These ideas come together as an ecosystem of resources and tools that highlight three key principles for design: modularity, forkability, and polycentricity. We end with a discussion of how workshop ideas might be implemented in future work aiming to support community governance in social computing more broadly.

  PublisherDOI

• DEEP: A Discourse Evolution Engine for Predictions about Social Movements

  ArXiv.org · 2025-11-03

  preprintOpen access

  Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025 that will be publicly released for research purposes upon publication of this paper.

  PublisherOA PDFDOI

Recent grants

• CHS: Small: Collaborative Research: Pathways to Community Success: Advancing a Comparative Science of Online Collaborative Organization

  NSF · $202k · 2016–2021

Frequent coauthors

• Benjamin Mako Hill

  78 shared

• Nathan TeBlunthuis

  University of Michigan–Ann Arbor

  37 shared

• Eszter Hargittai

  University of Zurich

  22 shared

• Elizabeth M. Gerber

  7 shared

• John J. Horton

  National Bureau of Economic Research

  7 shared

• Yochai Roberts Benkler

  Harvard University

  7 shared

• Daniel L. Chen

  6 shared

• Michael S. Bernstein

  5 shared

Labs

• Advancing Risk Communication in Health (ARCH) LabPI

Awards & honors

• Scholar in Residence, King County, Washington, 2022-2023
• Visiting Professor, Communication, University of Washington,…
• Faculty and Administrator Honor Roll, Northwestern Universit…
• Co-PI, National Science Foundation, IIS:CHS: Modeling the Ec…
• Lenore Annenberg and Wallis Annenberg Fellow in Communicatio…