Professor and Head
Department of Sociology and Criminology, Memorial University
mclevey@mun.ca
John McLevey (he/him) is Professor and Head of Sociology and Criminology at Memorial University, where he directs the Computational Social Science Lab. His research examines how social networks and institutional environments shape the formation and evolution of public opinion, scientific knowledge, and political belief systems. His work contributes to methodological foundations in computational social science, with an emphasis on Bayesian data analysis and generative models for multilevel networks and large-scale text data. He has also developed open-source research software to support transparent and reproducible scientific computing.
McLevey is the author of Doing Computational Social Science (Sage, 2022) and co-editor of The Sage Handbook of Social Network Analysis (Sage, 2023), with John Scott and Peter J. Carrington. His research has appeared in Social Networks, Scientometrics, Journal of Quantitative Criminology, Social Studies of Science, American Psychologist, and the Canadian Review of Sociology. His work has been supported by grants from the Social Sciences and Humanities Research Council of Canada (SSHRC) and an Early Researcher Award from the Ontario Ministry of Research and Innovation.
- McLevey, John, John Scott, and Peter J. Carrington, eds. 2023. The Sage Handbook of Social Network Analysis. Volume 2. London, UK: Sage.
- McLevey, John. 2022. Doing Computational Social Science. London, UK: Sage.
- Collins, Harry, Rob Evans, Martin Innes, Will Mason-Wilkes, Eric Kennedy, and John McLevey. 2022. The Face to Face Principle and the Internet: Science, Trust, Truth and Democracy. Cardiff, UK: Cardiff University Press.
- Graham, Alexander, John McLevey, Tyler Crick, and Pierson Browne. 2022. “Structural Diversity Is a Poor Proxy for Information Diversity: Evidence from 25 Scientific Fields.” Social Networks 70:55–63.
- McLevey, John, Tyler Crick, Pierson Browne, and Darrin Durant. 2022. “A New Method for Computational Cultural Cartography: From Neural Word Embeddings to Transformers and Bayesian Mixture Models.” Canadian Review of Sociology / Revue canadienne de sociologie 59(2):228–250.
- McLevey, John, and Tyler Crick. 2021. “Machine Learning and Neural Network Language Modelling for Sentiment Analysis.” In The Sage Handbook of Social Media Research, edited by Luke Sloan and Anabel Quan-Haase. London, UK: Sage.
- Disinformation, Democracy, and Online Political Deliberation (PI – John McLevey)
- Making Sense of Climate Action: Understanding Social Mobilization to Curb Anthropogenic Climate Change Through Advances in Social Network Analysis (PI – David Tindall)
- What Makes You Say That? Causal Inference for Cultural Cognition Political Belief Systems (PI – John McLevey)