Estimating Spatial Preferences from Votes and Text

Kim, In Song; Londregan, John; Ratkovic, Marc

doi:10.1017/pan.2018.7

Cited by 24 publications

(18 citation statements)

References 51 publications

(128 reference statements)

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“…Most importantly, Taddy (2013Taddy ( , 2015 made no attempt to define or quantify the divergence in language between groups either at a point in time or over time, nor did he discuss the finite-sample biases that arise in doing so. Our paper also relates to other work on measuring document partisanship, including Laver, Benoit, and Garry (2003), Groseclose and Milyo (2005), Gentzkow and Shapiro (2010), Kim, Londregan, and Ratkovic (2018), and Yan, Das, Lavoie, Li, and Sinclair (2018). 3 Our paper contributes a recipe for using statistical predictability in a probability model of speech as a metric of differences in partisan language between groups.…”

Section: Introductionmentioning

confidence: 64%

Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech

Gentzkow¹,

Shapiro²,

Taddy

2019

ECTA

308

213

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We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite‐sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century.

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Section: Introductionmentioning

confidence: 64%

Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech

Gentzkow¹,

Shapiro²,

Taddy

2019

ECTA

308

213

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“…Gerrish and Blei (2012) and Lauderdale and Clark ( 2014) use text and vote data to learn ideal points adjusted for topic. The models in Nguyen et al (2015) and Kim et al (2018) analyze votes and floor speeches together. With labeled political party affiliations, machine learning methods can also help map language to party membership.…”

Section: Related Workmentioning

confidence: 99%

Text-Based Ideal Points

Vafa¹,

Naidu²,

Blei³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Ideal point models analyze lawmakers' votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also give speeches, release press statements, and post tweets. In this paper, we introduce the text-based ideal point model (), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. We demonstrate the with two types of politicized text data: U.S. Senate speeches and senator tweets. Though the model does not analyze their votes or political affiliations, the separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. One benefit of analyzing texts, as opposed to votes, is that the can estimate ideal points of anyone who authors political texts, including non-voting actors. To this end, we use it to study tweets from the 2020 Democratic presidential candidates. Using only the texts of their tweets, it identifies them along an interpretable progressive-tomoderate spectrum.

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“…An increasing number of studies focuses on the determinants of the issue profile parties and their representatives adopt in their election manifestos, basic programs, or during the political process in parliament and government, for example by giving speeches, introducing bills, requesting roll call votes, making statements on social media platforms, or drafting press releases (see, for instance, Bräuninger et al, 2012;Ecker, 2017;Haselmayer et al, 2019;Kim et al, 2018;Meyer and Wagner, 2016;Proksch et al, 2019). Schröder and Stecker (2018) distinguish two theoretical perspectives on parties' issue competition strategies: the literature that focuses on issue ownership (e.g.…”

Section: Theory and Hypothesesmentioning

confidence: 99%

Strategic issue emphasis in parties’ election campaign statements

2019

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Parties should develop a consistent issue profile during an electoral campaign. Yet, manifestos, which form the baseline for a party’s programmatic goals in the upcoming legislative period, are usually published months before Election Day. We argue that parties must emphasize policy issues that are of key relevance to their likely voters in the last weeks of the election campaign, in which an increasing share of citizens make up their minds in terms of which party they will choose. To test this notion empirically, we draw on a novel data set that covers information on party representatives’ statements made during the final weeks of an election campaign in nine European countries. Focusing on the campaign messages of social democratic and socialist parties, we find that these parties indeed intensify their emphasis of unemployment policy, which is a salient issue for their core voter clienteles, particularly in times of economic hardship.

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Estimating Spatial Preferences from Votes and Text

Cited by 24 publications

References 51 publications

Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech

Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech

Text-Based Ideal Points

Strategic issue emphasis in parties’ election campaign statements

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