Past
Towards a Computational Social Science of Social Norms: Examples from Research with Emily Post’s Etiquette
Date & Time
02/04/2025 1:15 pm – 2:45 pm
Andrea Voyer
Stockholm University
Andrea Voyer is a Professor of Sociology at Stockholm University whose research explores cultural processes of inequality, with a focus on social inclusion, exclusion, and the construction of solidaristic communities. Her work addresses core sociological issues, including intergenerational economic inequality, immigrant integration, and the roles of race, ethnicity, and class in shaping social structures. Voyer connects micro-level behaviors and judgments with larger structures of inequality, employing a range of methods, including interviews, ethnographic research, and computational text analysis.
About the Event
What we learned from our DMI Seminar with Andrea Voyer:
Last week, the Data Methods Initiative hosted sociologist Andrea Voyer (Stockholm University) for a seminar that invited us to think differently about what constitutes “data.” Her talk “Towards a Computational Social Science of Social Norms: Examples from Research with Emily Post’s Etiquette” offered both a compelling methodological provocation and a fascinating empirical case.
Andrea’s starting point was deceptively simple: etiquette books. Often dismissed as outdated manuals of manners, these texts, are rich data sources that encode moral expectations, social boundaries, and shifting ideas of what it means to belong. Drawing from a century of Emily Post’s etiquette guides, her team created the Etiquette Corpus, a resource that lends itself to both close reading and large-scale computational analysis.
By bringing together sociological theory and tools like Concept Mover’s Distance, Andrea and her team showed how computational methods can surface patterns of inclusion and exclusion across time. From tracing how “immigrants” became “Americans,” to uncovering implicit expectations around class and gender, the analysis revealed how seemingly mundane advice on introductions, dinner parties, or thank-you notes, actually reflects deep structures of social hierarchy.
We also has a candid discussion about the process of building interdisciplinary teams, navigating methodological silos, and translating sociological questions into machine-readable form.
Andrea’s reflections were as insightful for those working with text data as they were for anyone thinking about norms, boundary-making, or the cultural production of difference. Whether you’re a qualitative researcher curious about computational tools or a data scientist looking for richer cultural context, this seminar offered a model for thoughtful, reflexive, and boundary-crossing work.
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Social Norms as Data
Etiquette books aren’t just cultural curiosities — they encode shifting social norms, moral boundaries, and ideas of belonging, making them valuable sources for historical and computational analysis. -
The Etiquette Corpus
Andrea’s team built a 100-year corpus of Emily Post’s etiquette books, enabling longitudinal study of how social rules and expectations change. The project involved careful digitization, cleaning, and annotation of complex, socially layered text. -
Concept Mover’s Distance in Practice
To track how ideas about social behavior evolved, Andrea used Concept Mover’s Distance, a method that compares meaning across texts by analyzing how concepts shift in semantic space over time. This approach helped surface long-term patterns in etiquette norms that traditional methods might miss. - Operationalizing Concepts
The study bridged the gap between abstract sociological concepts (like boundary-making) and computationally tractable proxies. Andrea discussed how team members collaborated to turn big theoretical questions into code-ready problems. - Working Across Methods
Andrea shared the challenges of doing work that resists the qualitative/quantitative divide, from rejections by journals to skepticism. She emphasized the value of meaning-focused analysis and the need to build communities that support boundary-crossing, methodologically mixed scholarship.
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