# How we do things with words: Analyzing text as social and cultural data

arXiv: https://arxiv.org/abs/1907.01468 · HTML: https://mlsanity.com/paper/1907.01468/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [Explaining Datasets in Words: Statistical Models with Natural Language Parameters](https://mlsanity.com/paper/2409.08466/) (2024)
- [What is a protest anyway? Codebook conceptualization is still a first-order concern in LLM-era classification](https://mlsanity.com/paper/2510.03541/) (2025)
- [TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora](https://mlsanity.com/paper/2103.11029/) (2021)
- [Applied Language Technology: NLP for the Humanities](https://mlsanity.com/paper/2105.01052/) (2021)
- [Probabilistic Embeddings with Laplacian Graph Priors](https://mlsanity.com/paper/2204.01846/) (2022)
- [Modular Domain Adaptation](https://mlsanity.com/paper/2204.14213/) (2022)
- [Ground Truths for the Humanities](https://mlsanity.com/paper/2103.12841/) (2021)
- [Democratizing Machine Learning for Interdisciplinary Scholars: Report on Organizing the NLP+CSS Online Tutorial Series](https://mlsanity.com/paper/2211.15971/) (2022)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)
- [The Differential Meaning of Models: A Framework for Analyzing the Structural Consequences of Semantic Modeling Decisions](https://mlsanity.com/paper/2509.00248/) (2025)

## 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.