A Scaling Model for Estimating Time‐Series Party Positions from Texts
Bibliographic Data
| ID | 6231038 |
|---|---|
| Authors | Jonathan B Slapin, Jonathan Slapin (0000-0002-6237-2868, Trinity College Dublin), Sven-Oliver Proksch (0000-0002-6130-6498, University of California, Los Angeles), Sven‐oliver Proksch |
| Year | 2008 |
| Volume | 52 |
| Issue | 3 |
| Pages | 705-722 |
| Publication date | 2008-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Political Science (JOURNAL) |
| Journal identifiers | ISSN: 0092-5853 • E-ISSN: 1540-5907 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/j.1540-5907.2008.00338.x |
| OpenAlex | W1998151601 |
| Language | EN |
| Citations received | 381 |
| References cited | 38 |
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
Algorithm · Data mining · Econometrics · German · Linguistics · Machine learning · Multidimensional scaling · Political science · Politics · Robustness (evolution · Scaling · Selection (genetic algorithm · Series (stratigraphy · Time series · Word (group theory · Artificial Intelligence · Computational and Text Analysis Methods · Computer Science · Electoral Systems and Political Participation · Law · Mathematics · Political Influence and Corporate Strategies
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| Unique citing works | 381 |
|---|---|
| Citations per year | 21,17 |
| Citation span | 2008 - 2026 (19) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 380 |