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From Text Signals to Simulations: A Review and Complement to <i>Text as Data</i> by Grimmer, Roberts &amp; Stewart (PUP 2022)

DSEID
DSEID-001-4504151
DOI
10.1177/00491241221123086
Journal
Sociological Methods &amp; Research
Publisher
SAGE Publications
Published
2022-11
Status
metadata_only

Abstract

Text as Data represents a major advance for teaching text analysis in the social sciences, digital humanities and data science by providing an integrated framework for how to conceptualize and deploy natural language processing techniques to enrich descriptive and causal analyses of social life in and from text. Here I review achievements of the book and highlight complementary paths not taken, including discussion of recent computational techniques like transformers, which have come to dominate automated language understanding and are just beginning to find their way into the careful research designs showcased in the book. These new methods not only highlight text as a signal from society, but textual models as simulations of society, which could fuel future advances in causal inference and experimentation. Text as Data's focus on textual discovery, measurement and inference points us toward this new frontier, cautioning us not to ignore, but build upon social scientific interpretation and theory.

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Metadata

Title
From Text Signals to Simulations: A Review and Complement to <i>Text as Data</i> by Grimmer, Roberts &amp; Stewart (PUP 2022)
Delta ID
DSEID-001-4504151
Authors
James Evans
Abstract source
crossref
Source URL
None
Access
closed_or_uncertain
Licence
unknown
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GROBID

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WhenEventFieldOldNew
2026-06-18 19:37:53.011249+00:00identifier_assignedDSEIDDSEID-001-4504151