Tina Law

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Computational Sociologist

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about

I am an Assistant Professor of Sociology at the University of California, Davis. I use and develop AI, natural language processing, and network methods and techniques to analyze text and images in order to address questions about social and political inequality in the U.S. I am particularly interested in leveraging computational methods and techniques to understand political meaning-making and how these processes are shaped by and alter socio-economic inequality.

I received my Ph.D. in sociology in 2022 from Northwestern University, where I was a National Science Foundation Graduate Research Fellow and Harvard Ash Center Democracy Visiting Fellow and trained with Andrew Papachristos (advisor), Mary Pattillo, and Joscha Legewie. Prior to joining UC Davis, I was a Postdoctoral Scholar with the Stone Center on Socio-Economic Inequality at the CUNY Graduate Center, where I trained with Leslie McCall.

I am also the co-founder of varyCSS, a volunteer collective of scholars in academia and industry who seek to advance and improve the field of computational social science by centering the work of scholars from historically marginalized communities. We maintain a Scholars Database and a Literature App on our website, varyCSS.org.

recent publications

Law, Tina and Elizabeth Roberto. "Generative Multimodal Models for Social Sceince: An Application with Satellite and Streetscape Imagery." Forthcoming. Sociological Methods & Research. [article]

This article introduces a methodological framework for analyzing images with generative multimodal models (gen MMs), artificial intelligence models that are trained to engage with multiple modalities (e.g., text, image, audio). We demonstrate this framework with an empirical application that uses OpenAI’s gpt-4o model to analyze satellite and streetscape images to identify built environment features that contribute to contemporary residential segregation in U.S. cities. We find that when provided with well-defined image labels, GPT-4o labels images with high validity compared to expert labels. We also provide recommendations for ensuring that the future of large-scale image analysis in the social sciences is not only rigorous and reproducible but also ethical and sustainable.

Than, Nga, Leanne Fan, Tina Law, Laura Nelson, and Leslie McCall. "Updating "The Future of Coding:" Qualitative Coding with Generative Large Language Models." Forthcoming. Sociological Methods & Research. [article]

This article tests the ability of generative large language models (LLMs) to replicate and augment traditional qualitative coding with an empirical application focused on identifying U.S. news articles published between 1980 and 2012 that discuss the complex concept of economic inequality. We find that generative LLMs can perform nearly as well as supervised machine learning (SML) models in accurately matching hand-coding output. We also find that propietary generative LLMs do not perform substantially better than open-source generative LLMs. This article extends previous work that analyzes this corpus with qualitative methods and SML, uniquely enabling direct comparisons of different text analysis methods.

Law, Tina and Leslie McCall. 2024. "AI Policymaking: An Agenda for Sociological Research." Socius, 10. [article]

This article calls for more robust and coordinated policy-oriented research on AI among sociologists. To support sociologists in this endeavor, we provide an overview of the rapidly evolving AI policy landscape, contrasting two leading approaches to AI governance: a safety-based vs. an equity-based approach. We argue that the safety-based approach is the predominant approach but is inadequate, and suggest that sociologists can shift AI policymaking to prioritize equity by examining corporate political power in AI policy debates and by organizing research around four sociological questions centered on equity and public engagement.

Kesari, Aniket, Jae Yeon Kim, Sono Shah, Taylor Brown, Tiago Ventura, and Tina Law (equal authorship). 2024. "Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers." PS: Political Science & Politics 57(1):101-106. [article]

This article shares the “hidden curriculum” of computational social science (CSS) training and professionalization for social science Ph.D. students. We outline core competencies and provide resources as it relates to three primary areas of CSS training and professionalization: (1) learning data science skills, (2) building a portfolio that uses data science to answer social science questions, and (3) connecting with computational social scientists. We also provide practical recommendations for departments and professional associations to better support Ph.D. students interested in CSS.

recent talks

I am currently available for in-person and remote talks during Fall 2025

teaching

Social Relationships

This course introduces undergraduate students to the sociological study of social networks, or how social networks can serve as, what sociologist Charles Kadushin calls, “problem-solving tools.” Social networks consist of a set of actors and relationships within a given context, and social network analysis is a theoretical and methodological approach that examines patterns of relationships between actors in order to understand pressing social issues. We will learn about social network theory and analysis and how to apply these concepts and tools to design a network-based research project. In particular, we will discuss examples of how social network theory and analysis can be used to address three important social topics: inequality, political behavior, and artificial intelligence.

Introduction to Social Statistics

This course introduces undergraduate students to quantitative sociological research, or the use of quantitative methods (i.e., tools based on numbers and involving software) to ask and answer research questions in order to better understand pressing social issues and potential interventions. In particular, we will focus on applying quantitative methods to examine poverty, inequality, and social mobility in the U.S. We will cover: research question formulation, sampling and measurement, probability and uncertainty, basic statistical inference, and hypothesis testing.