Computational and Text Analysis Methods

Computational text analysis applies methods from machine learning and natural language processing—such as topic modeling and automated classification—to make sense of large collections of written material that would be impractical to read by hand. Social scientists use these tools to trace how public discourse shifts over time, measure ideological change in political documents, or detect patterns in survey responses at scales impossible through traditional content analysis. A central challenge is ensuring that the statistical structures these methods surface actually correspond to meaningful social phenomena rather than artifacts of the data or the algorithm. Active work focuses on validating computational findings against human interpretation, adapting language models trained on general text to specialized social science corpora, and developing frameworks that make quantitative text evidence legible alongside conventional qualitative evidence.

Works

48,079

Total citations

151,432

Keywords

Computational Text AnalysisTopic ModelingMachine LearningSocial Science ResearchText Data MethodsQuantitative Analysis