Welcome to NETLAB, Dr. John McLevey's research group at the University of Waterloo. Our current SSHRC-funded research projects straddle several related areas, primarily:
- the micro- and macro-level processes through which opinions, beliefs, identities, worldviews, and lifestyle preferences form and evolve over the life course
- public opinion dynamics, lifestyle preferences and politics, and large-scale cultural change with an emphasis on mass polarization
- the workings and impacts of coordinated information operations such as disinformation campaigns and censorship on populations
Dr. McLevey is the Principal Investigator (PI) for several SSHRC-funded collaborative projects in these areas, with colleagues from Canada, the United Kingdom, Australia, Italy, Germany, and France. Much of this work focuses on opinions concerning collective risks (e.g., climate change, pandemics), science and expertise, surveillance and privacy, authoritarianism and populism, and lifestyle preferences and politics.
Our work in each of these three areas is informed by theory and research at the intersections of the social sciences (especially cultural and political sociology), cognitive science, and affective neuroscience, and by methods and models at the intersections of the social, computational, mathematical, and statistical sciences. Within computational social science and data science, our methodological expertise is mainly in:
- network science and social network analysis
- probabilistic and generative modelling (mainly empirically calibrated agent-based simulation models and Bayesian latent variable models)
- computational text analysis
Interested in Joining Us?
If you are interested in working in NETLAB or pursing a graduate degree at the University of Waterloo, please send me an email explaining why you are interested in working together and what your educational and / or professional background is. Although there are no open positions right now, there may be in the near future.
Using recordlinkage for classifying candidate record pairs.
Learning how to extract and explore records from raw bibliometric data.
Generate and analyze networks with metaknowledge.