The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
How to Analyze Political Attention with Minimal Assumptions and Costs
Open accessAbstract
Previous methods of analyzing the substance of political attention have had to make several restrictive assumptions or been prohibitively costly when applied to large‐scale political texts. Here, we describe a topic model for legislative speech, a statistical learning model that uses word choices to infer topical categories covered in a set of speeches and to identify the topic of specific speeches. Our method estimates, rather than assumes, the substance of topics, the keywords that identify topics, and the hierarchical nesting of topics. We use the topic model to examine the agenda in the U.S. Senate from 1997 to 2004. Using a new database of over 118,000 speeches (70,000,000 words) from the Congressional Record, our model reveals speech topic categories that are both distinctive and meaningfully interrelated and a richer view of democratic agenda dynamics than had previously been possible.
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“Political scientists have extended topic models so that the parameters correspond to politically relevant quantities of interest.”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010
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“Quinn et al. (2010) argue that if topics are valid, then external events should explain sudden increases in attention to a topic”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010
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“As Quinn et al. (2010) observe, unsupervised methods shift the user burden from determining categories before analysis to validating model output afterward.”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010
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“The first topic model in political science is the dynamic multitopic model (Quinn et al. 2010), used to model the daily attention to topics in Senate floor speeches”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010
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“In contrast to computer scientists, who focus primarily on out-ofsample prediction, quantitative social scientists typically test their assumptions by assessing construct reliability and construct validity.”
From Measurement and Fairness (2021) · cites this paper as [49]
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Self-citation
Metadata
- Authors
- Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R.
- Affiliations
- University of California, Berkeley (US)
- Pennsylvania State University (US)
- Michigan State University (US)
- University of Georgia (US)
- University of Michigan (US)
- Venue
- American Journal of Political Science
- Publisher
- Wiley
- Published
- 2009-12-28
- Volume
- 54
- Issue
- 1
- Pages
- 209-228
- ISSN
- 0092-5853 (ISSN-L)
- 1540-5907
- OpenAlex
- W2144874182
- Cited by
- 671
- References
- 70
- Citations by year
- 2026: 21 · 2025: 48 · 2024: 40 · 2023: 57 · 2022: 55 · 2021: 57 · 2020: 64 · 2019: 60 · 2018: 52 · 2017: 42 · 2016: 36 · 2015: 41 · 2014: 35 · 2013: 20 · 2012: 15
- FWCI
- 72.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- onlinelibrary.wiley.com/termsAndConditions#vor (vor)
- Locations
- American Journal of Political Science (publishedVersion, public-domain)
- Funders
- National Science Foundation (0527513, 0714688)
- Center for Advanced Study in the Behavioral Sciences, Stanford University
- Center for Advanced Study, University of Illinois at Urbana-Champaign
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- political attention, parliamentary speeches, topic modeling, topic hierarchy, United States Senate, congressional records, statistical learning, word choice
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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