How to Analyze Political Attention with Minimal Assumptions and Costs

By Kevin M. Quinn, Burt L. Monroe, Michael Colaresi, Michael H. Crespin, Dragomir R. Radev. 2010

Previous methods of analyzing the substance of political attention have had to make several restrictive assumptions or been prohibitively costly when applied to large-scale political texts. Here, we describe a topic model for legislative speech, a statistical learning model that uses word choices to infer topical categories covered in a set of speeches and to identify the topic of specific speeches. Our method estimates, rather than assumes, the substance of topics, the keywords that identify topics, and the hierarchical nesting of topics. We use the topic model to examine the agenda in the U.S. Senate from 1997 to 2004.Usinganewdatabaseofover118,000speeches(70,000,000words)fromtheCongressional Record,ourmodelreveals speech topic categories that are both distinctive and meaningfully interrelated and a richer view of democratic agenda dynamics than had previously been possible.

Subfields: Social Sciences & Humanities

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Citations in the corpus

Measurement and Fairness (2019, methodology): “lidity, but it is common to group all of these types of validity under the umbrella of construct validity, as we do here. We situate our framework largely with the traditions laid out by Quinn et al. [56], Messick [44], and Loevinger [42]. Specically, we unite traditions from political science, educ”
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets (2021, methodology): “Measurement Modeling We apply a measurement modeling lens by viewing each benchmark as a measurement model (MM) (e.g., Quinn et al., 2010; Jacobs and Wallach, 2021).”
Missing Data Imputation for Supervised Learning (2016, background): “al applications of supervised learning include the estimation of heterogeneous treatment eﬀects (Imai and Strauss, 2011; Green and Kern, 2012; Imai et al., 2013; Grimmer et al., 2014), text analysis (Quinn et al., 2010; Hopkins and King, 2010; Grimmer and Stewart, 2013; Lauderdale and Clark, 2014; W”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “(Trochim and Donnelly, 2001; Quinn et al., 2010; Howison et al., 2011; Lazer, 2015).”
Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach (2018, unknown): “First, following the procedure in Quinn et al. (2010), Atkins et al. (2012), and Bao and Datta (2012), we manually read the high-probability words in key topics and their respective sentences, to provide a short and intuitive label for each topic.”
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures (2014, background): “Rogers et al. (2011) use both general-purpose and context-specific text dictionaries to quantify optimistic and pessimistic tones in firms’ earnings announcements.”
Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict (2008, methodology): “That is, the visualizations reflect either a selection of words or a word-specific measure that is intended to characterize some semantic political concept of direct interest, for example, topic (Quinn et al. 2006), ideology (Diermeier et al. 2007), or competitive frame (Schonhardt-Bailey 2008).”
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013, methodology): “6.4 Validating Unsupervised Methods: How Legislators Present Their Work to Constituents As Quinn et al. (2010) observe, unsupervised methods shift the user burden from determining categories before analysis to validating model output afterward.”
How to make causal inferences using texts (2018, unknown): “While validation approaches may vary—this necessarily involves reading documents ( 15 , 22 , 29 )—it is helpful to fix a set of human-assigned labels, example documents, and automated keyword labels in advance to avoid subtle influence from the test set.”
stm: An R Package for Structural Topic Models (2019, background): “…Hewlett Foundation, a National Science extended these (Mimno and McCallum 2008; Socher et al. 2009; Eisenstein et al. 2010; Rosen-Zvi et al. 2010; Quinn et al. 2010; Ahmed and Xing 2010; Grimmer 2010; Eisenstein et al. 2011; Gerrish and Blei 2012; Foulds et al. 2015; Paul and Dredze 2015), the…”
Text as Data (2017, methodology): “Another specification that is popular in political science (e.g., Quinn et al. 2010) keeps θ l as Dirichlet-distributed but requires each document to have a single topic.”
A Method of Automated Nonparametric Content Analysis for Social Science (2010, background): “” Preprocessing text strips out information, in addition to reducing complexity, but experience in this literature is that the trade-off is well worth it (Porter 1980; Quinn et al. 2009).”
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology (2018, methodology): “Two criteria of validity were checked: intra-topic and inter-topic semantic validity (Quinn et al., 2010).”
Transparency and deliberation within the FOMC: a computational linguistics approach (2014, background): “Conformity/Non-conformity Scharfstein and Stein (1990) show that agents with career concerns unsure of their expertise tend to herd on the same action, thereby avoiding being the only one to take an incorrect decision.”
Measuring Polarization with Text Analysis: Evidence from the UK House of Commons, 1811–2015 (2019, methodology): “However, as (Quinn et al. 2010, 216) rightly note, “[f]ace validity is inherently subjective, generally viewed as self-evident by authors and with practiced skepticism by readers”.”
A Model of Text for Experimentation in the Social Sciences (2016, methodology): “This problem has motivated the development of a series of application-specific models designed to capture particular quantities of interest (Grimmer 2010; Quinn et al. 2010; Gerrish and Blei 2012; Ahmed and Xing 2010).”