I am an Associate Professor in the Department of Knowledge Integration at University of Waterloo, and am cross-appointed to Sociology & Legal Studies, the School of Environment, Resources, & Sustainability (SERS), and Geography & Environmental Management (GEM). I’m also a Policy Fellow at the Balsillie School of International Affairs and a Member of the Cybersecurity and Privacy Institute at the University of Waterloo.

My primarily research interests are in computational social science, with a particular emphasis on social network analysis and applications of natural language processing. Substantively, my research interests are in (1) democracy and the politics of information, science, and knowledge, (2) cognitive social science (also sometimes called cognitive sociology / culture and cognition / or the sociology of knowledge), and (3) environmental governance and social movements.

My work is funded by research grants from the Social Science and Humanities Research Council of Canada (SSHRC) and an Early Researcher Award from the Ontario Ministry of Research and Innovation. On the teaching side of things, I’m especially passionate about teaching research methods and courses that integrate the social sciences and data science.

You can view my full CV here.


Doing Computational Social Science

John McLevey. Forthcoming 2020 from Sage (London, UK).

Industrial Development and Eco-Tourisms

Mark Stoddart, Alice Mattoni, and John McLevey. Forthcoming 2021 from Palgrave MacMillan.

Face-to-Face. Communication and the Liquidity of Knowledge

Harry Collins, Rob Evans, Martin Innes, Eric Kennedy, Will Mason-Wilkes, and John McLevey. Full manuscript under review.

Research Software

My students and I (NetLab) develop open source software for research in computational social science. Our primary focus is on (1) combining network analysis and applied natural language processing, (2) developing semi-automated methods for reviewing and synthesizing large bodies of scientific research, and (3) developing tools to make collaborative computational social science more open, transparent, and reproducible. From time to time, we also work on problems related to data collection and record linkage.


Nate - A Python package for research at the intersection of network analysis and applied natural language processing.
metaknowledge - A Python package for constructing various kinds of quantitative and network datasets from the Web of Science, Scopus, PubMed (MEDLINE), and other databases.
pdpp - A Python package simplifying principled data processing workflows.
gitnet - A Python package for constructing collaboration networks from git repositories.


The Art & Science of Learning

INTEG 120 -- Offered every fall semester

There are three sets of related learning objectives in this course. First, you will understand how learning actually works by engaging with state-of-the-art scientific research on learning and cognitive adaptability. Second, you will begin using this research to become more intentional learners and knowledge integrators by cultivating effective habits and mindsets, and by balancing focused and deliberate practice with deliberate rest. Third, you will learn to appreciate the limits of your own individual knowledge and skills and to leverage the power of diversity and collective intelligence. We will end by putting all of these pieces together into explicit frameworks and strategies for learning and the integration and synthesis of different types of knowledge. To accomplish these learning objectives, (1) we will read and discuss work by cognitive scientists and social scientists and (2) complete a series of assignments that are designed with the goal of helping you develop your skills as a learner and knowledge integrator. This course is just the beginning, but by the end of it, you will have at your disposal essential skills for learning, thinking, and behaving in integrative ways.

Bullshit, Bias, & Bad Arguments

INTEG 240 -- Online course in development

This course is an introduction to information literacy and the art and science of spotting, understanding, and talking constructively about bullshit and other types of false claims. The course will cover a broad range of issues, such as logical fallacies, misrepresentations of science, comparisons of how true and false claims spread in mainstream and social media, and tactics used to manipulate and influence people and public debates. The course emphasizes fundamental skills that are important for countering bullshit and other false claims.

Research Methods & Design

INTEG 340 -- Offered every fall semester

This course provides an introduction to empirical research design and methods with a focus on applications in the social sciences and related fields. You will learn about core issues in research design (e.g. operationalization, sampling, ethics) that transcend specific approaches, and about a variety of techniques for collecting and analyzing quantitative and qualitative data. The course will cover both abstract and practical issues related to methodology and decision making in empirical research. By the end of the course you will be a more informed consumer and have a basic set of skills for designing and implementing your own empirical research projects. Most importantly, you will have a foundation for future learning about research design and methods.

Computational Social Science

INTEG 440/640 -- Online course, offered every winter semester

The explosion of digital data is revolutionizing the way we learn about the world. This course focuses on the knowledge and skills necessary for doing high-quality social scientific research with digital data. Students will be introduced to the programming language Python, and will learn to collect and analyze digital data using computational methods.

Senior Honours Project

INTEG 420 -- Offered every fall and winter semester

The KI Senior Honours Project is the two-term culmination of the Knowledge Integration degree. It provides you with the opportunity to engage in an original research or design project advised by an expert (or experts) in your chosen field. Regardless of the specific project you do, you will need to draw on a broad range of skills and knowledge acquired during your Knowledge Integration degree, including design thinking, competently using research methods to answer questions, systematically reviewing literature, learning strategically and efficiently with minimal guidance, communicating complex ideas clearly, working with stakeholder groups, and breaking large projects into small achievable bits. Your advisor(s) and I will be holding you to a high standard.