How we do things with words: Analyzing text as social and cultural data
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Abstract (arXiv)
In this article we describe our experiences with computational text analysis. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of best practices for working with thick social and cultural concepts. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that will resonate for many. And this leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis that involves social and cultural concepts, and the more we are able to bridge these divides, the more fruitful we believe our work will be.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2019.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 2; 2025: 2.
11 papers.
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptionscited by 61
- 2024Explaining Datasets in Words: Statistical Models with Natural Language Parameterscited by 21
- 2025What is a protest anyway? Codebook conceptualization is still a first-order concern in LLM-era classificationcited by 2
- 2021TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corporacited by 1
- 2021Applied Language Technology: NLP for the Humanitiescited by 1
- 2022Probabilistic Embeddings with Laplacian Graph Priorscited by 1
- 2022Modular Domain Adaptationcited by 1
- 2021Ground Truths for the Humanitiescited by 0
- 2022Democratizing Machine Learning for Interdisciplinary Scholars: Report on Organizing the NLP+CSS Online Tutorial Seriescited by 0
- 2024Beyond Predictive Algorithms in Child Welfarecited by 0
- 2025The Differential Meaning of Models: A Framework for Analyzing the Structural Consequences of Semantic Modeling Decisionscited by 0
- 2025What is a protest anyway? Codebook conceptualization is still a first-order concern in LLM-era classification3 Oct 2025
- 2025The Differential Meaning of Models: A Framework for Analyzing the Structural Consequences of Semantic Modeling Decisions29 Aug 2025
- 2024Explaining Datasets in Words: Statistical Models with Natural Language Parameters13 Sep 2024
- 2024Beyond Predictive Algorithms in Child Welfare26 Feb 2024
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptions28 Feb 2023
- 2022Democratizing Machine Learning for Interdisciplinary Scholars: Report on Organizing the NLP+CSS Online Tutorial Series29 Nov 2022
- 2022Modular Domain Adaptation26 Apr 2022
- 2022Probabilistic Embeddings with Laplacian Graph Priors25 Mar 2022
- 2021Applied Language Technology: NLP for the Humanities3 May 2021
- 2021Ground Truths for the Humanities23 Mar 2021
- 2021TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora19 Mar 2021