I (Chinese: 张勇军audio) am a computational sociologist who develops and applies natural language processing and computer vision techniques with big data to study political, social, and organizational behavior. I received my Sociology PhD in 2020 from the University of Arizona. I am currently working as an Assistant Professor of Sociology and Institute for Advanced Computational Science at Stony Brook University. I am also a research affiliate at New York University.

My previous work explored the dynamic relationship between social movements and socio-political changes both in the United States and globally. For instance, my undergraduate thesis was an ethnographic study of the state repression of petitioners in Beijing based on the participant observation of the Dong Zhuang village; my MA thesis used quantitative data to understand how relative deprivation influences protest propensity in China. Some of my social movement-related studies were published in Journal of Marriage and Family, Demography, Poetics, International Journal of Comparative Sociology, American Journal of Sociology, and PLoS One. I won the 2020 James Coleman Award from Sociology of Education Section at American Sociological Association and the 2021 SIM Best Paper Submission at Academy of Management. Currently, I am extending this line to visual representation of global protests by examining millions of images from newspapers worldwide with computer vision techniques and large language/vision models.

My current work focuses primarily on understanding mobility, segregation, and polarization in the U.S. I am using big data from FEC with corp data to track the polarization/partisanship trend in the US corporate leadership. I am using large-scale GPS data and near-population level voter and consumer records to assess the antecedents and consequences of racial/partisan/income/cultural segregation in both residential and activity spaces. This human mobility, segregation, and polarization project has been funded by an OVPR seed grant at Stony Brook University. I am also using deep learning methods to detect and monitor anti-AAPI hate speech and incidents as well as associated mental health issues (a direct result of polarization and xenophobia) from Twitter since the COVID-19 outbreak. This project has been funded by a seed grant from IACS at Stony Brook University. Some of these ongoing studies have appeared in top journals such as Demography, RSF: Journal of Social Sciences, the Sociological Quarterly, Chinese Sociological Review, Socius, Scientific Reports, and Nature Humanities and Social Sciences Communications.

I am also starting to expand my research into new areas (Yes, I like new challenges), including the use of AI in addressing climate and health issues. Specifically, I am keen on exploring a significant question: How can we leverage the rapid advancements in AI to confront critical challenges such as conflict and violence and climate change to improve community resilience? If you are interested in these questions, feel free to reach out to me. I am seeking any potential collaboration in the near future. Building on large-scale multimodal social media data such as Twitter (now X) and YouTube, we are developing and applying large language and vision models to capture the psychological state of online social media users and further assess how it might shape online and offline behavior. We are also interested in the discourse opportunity structure of enviromental justices across various social media platforms over time and how AIs can help mitigate misinformation on these platforms.

I am teaching Intro to Computational Social Science and Research Methods in Sociology at Stony Brook University. I co-edited a special issue on computational social science and Chinese societies for Chinese Sociological Review. I also co-organized the 2023 NYU Shanghai site for Summer Institute of Computational Social Science (SICSS).

If you need CSS-related talks or workshops, please feel free to contact me directly.

Download Zhang’s Vita Here.

Incoming Students

If you are interested in working with me at Stony Brook, feel free to apply for our program. You can also check this amazing IACS graduate fellowship.

Summer CSS Workshop

I co-organized the 2023 NYU Shanghai site for Summer Institute of Computational Social Science (SICSS) with Dr. Xiaogang Wu and Dr. Yongren Shi. See HERE

I also taught intro to css for NYU Shanghai CASER. Check here for the syllabus.

Frontiers Conversations on Digital Society

I am co-hosting a Brownbag series with Professor Zeqi Qiu at Peking University focusing on digital society and its impact on our world. Our first speaker is Professor Chris Bail.

April 6, 2022, Conversation with Professor Chris Bail at Duke University, Director of Polarization Lab. Click here for more info.

November 10, 2022, Conversation with Professor Esteban Moro at MIT Media Lab. Sponsored by IACS at Stony Brook University.

November 4, 2022, Conversation with Assitant Professor Rebecca Johnson at Georgetown McCourt School of Public Policy. Sponsored by Sociology at Stony Brook University.

New Paper Alert

Siwei Cheng, Yongjun Zhang, and Jenna Shaw. 2024. ‘‘The Geography of Activity Space Segregation: Combining Mobile Device Data and Census Data.’’ RSF Journal of the Social Sciences.

Thomas V. Maher, Charles Seguin, and Yongjun Zhang. 2024. ‘‘The Racial Limits of Disruption: How Race and Tactics Influence Social Movement Organization Testimony Before Congress, 1960-1995.’’ Social Forces.

Yongjun Zhang and Jennifer Heerwig. 2024. ‘‘Gender, Race, and Intersectionality in the Political Donations of America’s Corporate Elite.” The Sociological Quarterly.

Working Paper

Yongjun Zhang. 2023. “LLVMs4Protest: Harnessing the Power of Large Language and Vision Models for Deciphering Protests in the News.” (Fine-tuned models available here)

Yongjun Zhang. 2023. “Generative AI has lowered the barriers to computational social sciences.”

Yongjun Zhang. 2023. “Multiplex Spatial Segregation of Asian American Voters in New York City.”

Yongjun Zhang and Siwei Cheng. 2023. “Mobility-based Segregation in U.S. Metropolitan Areas.”

Yongjun Zhang, Sijia Liu, Yi Wang, and Xinguang Fan. 2022. ‘‘Detecting Fake News on Twitter in the Chinese Language Community.”

Yongjun Zhang. 2021. “Using Population Mobility to Measure Racial Residential Segregation in the U.S. Metro Areas.”


Yongjun Zhang. Feb 2024. ‘‘Sinophobia on Western Social Media During the Early Pandemic.’’ York Centre for Asian Research. [Poster]; [Link]

Yongjun Zhang. Novemeber 2023. “Generative AI and Computational Social Science.” Peking University.

Yongjun Zhang. October 2023. “Quantifying Segregation with Big Data.” Department of Sociology at Queens College. [Poster]

Yongjun Zhang. August 2023. “Experienced Partisan Segregation.” ASA Session on Inequality across Time, Space, and Families.

Yongjun Zhang. July 2023. “Frontiers in Computational Social Science: New Trends, Opportunities, and Challenges.” Nanjing University [Poster].

Yongjun Zhang. June 2023. “An Overview of Computational Social Science”. SICSS NYU-Shanghai.

Yongjun Zhang. March 2022. “Gender, Race, and Intersectionality in the Political Donations of America’s Corporate Elite”. European University Institute. with Dr. Jennifer Heerwig.

Yongjun Zhang. Dec 2021. “Computational Social Science: Its Recent Development, Opportunities, and Challenges.” Peking University [Poster].

Yongjun Zhang. Oct 2021. “Residential Segregation in U.S. Metro Areas: Using Facebook and Safegraph Data to Assess Racial Segregation.” Department of Sociology, SUNY Buffalo [Poster].

Yongjun Zhang. Oct 2021. “Using Population Mobility Data to Measure Racial Segregation in the U.S.” IACS, Stony Brook University.

Yongjun Zhang. Sept 24, 2021. “Using Relational Data from Facebook and SafeGraph to Measure Racial Segregation.” Department of Physchology, Stony Brook University.

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