A Scaling Model for Estimating Time‐Series Party Positions from Texts
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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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The wordfish estimates in the left-hand plot of Fig. 7 separate the German parties and replicate expert assessments of German party movement over time.
“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
Monroe and Maeda (2004) and later Slapin and Proksch (2008) introduced statistical models based on item response theory (IRT) to automatically estimate the spatial location of parties.
“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
The authors replicate an example from Slapin and Proksch (2008) showing that the ideological dominance model works well with German political platforms.
“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
Slapin and Proksch (2008) developed wordfish as a Poisson-IRT model.
“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
A second method for scaling political actors using texts is an unsupervised method for locating actors in space.
“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
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- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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