To Measure Meaning in Big Data, Don’t Give Me a Map, Give Me Transparency and Reproducibility

Abstract

In this comment, I review the many subjective decisions embedded in cultural cartography to reframe the strength of computational methods in terms of transparency and replicability, not statistical objectivity. In an attempt to reorient the field toward a new standard for measuring meaning in big data, one based on transparency and replicability, I propose five guidelines to evaluate any text-analysis project.

Laura K. Nelson

Associate Professor of Sociology

I use computational methods to study social movements, culture, gender, institutions, and the history of feminism. I’m particularly interested in developing transparent and reproducible text analysis methods for sociology using open-source tools.