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Computational analysis offers new ways to derive meaning from text. We use large corpora of text as social sensors to measure what people feel, think, and talk about, which allows us to track the emergence of shared social understandings.

In October 2025, Marc Keuschnigg presented his research on combating online misinformation at the Centro de Estudios Públicos in Santiago de Chile.

New research from ¸£Àû¼§ shows that words in the U.S. Congress don’t just reflect politics – they predict it. Hendrik Erz reveals how speech and voting together shape American democracy.

Researchers from sociology and statistics implement a scalable seeded topic model that extracts interpretable meaning structures in perhaps the largest text corpus ever analyzed in the social sciences.

Dr. Anastasia Menshikova defended her PhD on May 19, exploring cultural change in immigration discourse through computational text analysis. Her thesis is the second completed within the Mining for Meaning research environment.

Analytical sociologists are harnessing troves of digitized text, digital trace, and social network data—along with the computational tools for their analysis—to answer sociology’s core questions in novel and rigorous ways.

As large corpora of digitized text become increasingly available, sociologists are rediscovering the potential of text data for inquiries into social and cultural phenomena.

An article shows how social scientists can leverage recent advances in natural language processing to automatically yet finely annotate millions of texts. They also provide a package and a tutorial for the open use of these methods.

In October 2019, we hosted scholars from across disciplines to discuss cutting-edge techniques for using large text corpora to understand how issues of migration and immigration are discussed in the political, media, and public spheres.