A Scaling Model for Estimating Time-Series Party Positions from Texts

By Jonathan B. Slapin, Sven-Oliver Proksch. 2007

750 citations recorded by the source corpus.

Subfields: General Social Sciences, Political Science and International Relations, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/5109c519cd4442041a5d3915ca305eba6d68ee10

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Citations in the corpus

Issue Emphases, Saliency Theory and Issue Ownership: A Historical and Conceptual Analysis (2015, background): “Computer word counts also make the connection between particular issue-related terms and specific parties (e.g. Slapin and Proksch 2008).”
Measuring Polarization with Text Analysis: Evidence from the UK House of Commons, 1811–2015 (2019, methodology): “…Wordfish—is a more appropriate technique (Lowe 2013), in practice, Wordfish is more robust to outliers in word use (Lauderdale and Herzog 2016).10 Wordfish is a statistical model that allows users to estimate a latent position of an actor on the basis of word frequencies (Slapin and Proksch 2008).”
Computer-Assisted Text Analysis for Comparative Politics (2015, methodology): “including processing and analyzing text in multiple languages and incorporating data we have Gerner 1994; Laver, Benoit, and Garry 2003; Slapin and Proksch 2008).”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, background): “conservative to liberal) have been predicted based on language using supervised techniques [58] or unsupervised inference of ideological space [59,60].”
Understanding the Political Ideology of Legislators from Social Media Images (2019, background): “In more recent work, however, there are been greater interest in developing more general methods for measuring political ideology (Grimmer and Stewart, 2013, Iyyer et al., 2014, Sim et al., 2013, Slapin and Proksch, 2008).”
Do parties matter in internationalised policy areas? The impact of political parties on environmental policy outputs in 18 OECD countries, 1970-2000 (2010, methodology): “…al. 2008) and a number of alternative methods for estimating the positions of political actors exist (e.g., Laver & Garry 2000; Laver et al. 2003; Slapin & Proksch 2008 ), the manifesto data of the CMP covers the programmatic statements of all relevant parties in the OECD countries over a long…”
Measuring Populism: Comparing Two Methods of Content Analysis (2011, background): “Wordfish works well for extracting single left–right dimensions (Slapin and Proksch 2008), while it is less suited to explore a specific ideological aspect such as populism.”
Politicization and Polarization in COVID-19 News Coverage (2020, methodology): “To be clear: The dimension identified by Wordfish is an undefined latent dimension based on the language used in all documents, and the correlation between this dimension and partisanship indicates the degree to which the language surrounding Democratic mentions is different from the language surrou”
quanteda: An R package for the quantitative analysis of textual data (2018, methodology): “Its textmodel_* functions provide native, highly efficient implementations of several text analytic scaling methods, such as Wordscores (Laver, Benoit, & Garry, 2003), Wordfish (Slapin & Proksch, 2008), class affinity scaling (Perry & Benoit, 2017), and correspondence analysis (Greenacre, 1984).”
Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict (2008, background): “One example is given by Slapin and Proksch (2008), made more striking because they provide a plot that clearly demonstrates the heteroskedasticity.”