The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
A Scaling Model for Estimating Time‐Series Party Positions from Texts
Open accessAbstract
Recent advances in computational content analysis have provided scholars promising new ways for estimating party positions. However, existing text‐based methods face challenges in producing valid and reliable time‐series data. This article proposes a scaling algorithm called WORDFISH to estimate policy positions based on word frequencies in texts. The technique allows researchers to locate parties in one or multiple elections. We demonstrate the algorithm by estimating the positions of German political parties from 1990 to 2005 using word frequencies in party manifestos. The extracted positions reflect changes in the party system more accurately than existing time‐series estimates. In addition, the method allows researchers to examine which words are important for placing parties on the left and on the right. We find that words with strong political connotations are the best discriminators between parties. Finally, a series of robustness checks demonstrate that the estimated positions are insensitive to distributional assumptions and document selection.
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In the citing paper
“As Slapin and Proksch (2008) show, the estimates in the left-hand plot separate the German parties and replicate expert assessments of German party movement over time.”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Slapin and Proksch 2008
In the citing paper
“First Monroe and Maeda (2004), then later Slapin and Proksch (2008), introduce statistical models based on item response theory (IRT) to automatically estimate the spatial location of the parties.”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Slapin and Proksch 2008
In the citing paper
“we replicate an example from Slapin and Proksch (2008) that shows that the model works well with German political platforms.”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Slapin and Proksch 2008
In the citing paper
“Slapin and Proksch (2008) develop their method, wordfish, as a Poisson-IRT model.”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Slapin and Proksch 2008
In the citing paper
“A second method is an unsupervised method for locating actors in space (Monroe and Maeda 2004;Slapin and Proksch 2008).”
From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Slapin and Proksch 2008
Citations
Cites
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Giné, V. L., & Tsebelis, G. (2003) · Revista Española de Investigaciones Sociológicas · doi:10.2307/40184562 · Cited by 3,361
A comparison of event models for naive bayes text classification
McCallum, A., & Nigam, K. (1998) · Cited by 3,289
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Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140
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Garrett, G. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511625633 · Cited by 1,864
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Benoit, K., & Laver, M. (2006) · doi:10.4324/9780203028179 · Cited by 1,616
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- De Swaan Abram, 1973, Coalition Theories and Cabinet Formation
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Cited by
Sorted by citations
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
quanteda: An R package for the quantitative analysis of textual data
Benoit, K. R., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00774 · Cited by 1,511
Politicization and Polarization in COVID-19 News Coverage
Hart, P. S., Chinn, S., & Soroka, S. (2020) · Science Communication · doi:10.1177/1075547020950735 · Cited by 715
How to analyze political attention with minimal assumptions and costs
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00427.x · Cited by 671
Metadata
- Authors
- Slapin, J. B., & Proksch, S.
- Affiliations
- Trinity College Dublin (IE)
- University of California, Los Angeles (US)
- Venue
- American Journal of Political Science
- Publisher
- Wiley
- Published
- 2008-07
- Volume
- 52
- Issue
- 3
- Pages
- 705-722
- ISSN
- 0092-5853 (ISSN-L)
- 1540-5907
- OpenAlex
- W1998151601
- Cited by
- 684
- References
- 52
- Citations by year
- 2026: 40 · 2025: 30 · 2024: 44 · 2023: 32 · 2022: 35 · 2021: 43 · 2020: 47 · 2019: 52 · 2018: 44 · 2017: 45 · 2016: 38 · 2015: 34 · 2014: 38 · 2013: 50 · 2012: 29
- FWCI
- 143.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Political Science (publishedVersion)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- party positions, policy positions, word frequency, party manifestos, time series estimation, German political parties, party system change, political connotation, left-right positioning
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