Please join us for a UW Data Science Seminar event on Tuesday, November 15th from 4:30 to 5:20 p.m. PDT. The seminar will feature Maximilian Puelma Touzel, Research Associate at Mila, Québec AI Institute/Université of Montréal.
“Topic correlation networks inferred from open-ended survey responses reveal signatures of ideology behind carbon tax opinion”
Abstract: Ideology can often render policy design ineffective by overriding what, at face value, are rational incentives. A timely example is carbon pricing, whose public support is strongly influenced by ideology. As a system of ideas, ideology expresses itself in the way people explain themselves and the world. As an object of study, ideology is then amenable to a generative modelling approach within the text-as-data paradigm. Here, we analyze the structure of ideology underlying carbon tax opinion using topic models. An idea, termed a topic, is operationalized as the fixed set of proportions with which words are used when talking about it. We characterize ideology through the relational structure between topics. To access this latent structure, we use the highly expressive Structural Topic Model to infer topics and the weights with which individual opinions mix topics. We fit the model to a large dataset of open-ended survey responses of Canadians elaborating on their support of or opposition to the tax. We propose and evaluate statistical measures of ideology in our data, such as dimensionality and heterogeneity, and explore their variation over topic phylogenies in time and in the assumed number of latent topics. Finally, we discuss the implications of the results for transition policy in particular, and of our approach to revealing ideology for computational social science and public policy in general.
Biography: Maximilian Puelma Touzel is a research associate at Mila and the University of Montréal working at the intersection of data science, multi-agent system models, and the human behavioural sciences. He develops theory and data-driven models in collaboration across the disciplines and scales of biological systems. Max obtained degrees in math and physics at the University of Toronto and a PhD from the Max Planck School for the Physics of Biological and Complex Systems at University of Goettingen.
The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.
The 2022-2023 seminars will be virtual, and are free and open to the public.