How to Analyze Political Attention with Minimal Assumptions and Costs

Quinn, Kevin M.; Monroe, Burt L.; Colaresi, Michael P.; Crespin, Michael H.; Radev, Dragomir

doi:10.1111/j.1540-5907.2009.00427.x

Cited by 524 publications

(453 citation statements)

References 39 publications

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“…To connect our analysis with the theory of Meguid (2005Meguid ( , 2008 we want to analyze the agenda and the framing of the political speeches. To do this we use a probabilistic topic model approach, similar in spirit to that of Quinn et al (2010). Topic models, such as the popular Latent Dirichlet Allocation by Blei et al (2003), are a way of modeling latent semantic themes without any manual classification, but with similarities to qualitative text methods such as grounded theory Baumer et al (2017).…”

Section: Methodsmentioning

confidence: 99%

“…The basic LDA model has been developed further to handle political corpora; in the work by Grimmer (2010) the model is extended to capture the agenda of Senate press releases, Quinn et al (2010) use a dynamic topic model to analyze legislative speech over time, and Greene & Cross (2015) use a non-negative matrix factorization approach to study how the agenda of the European Parliament changes over time (for more examples of topic models used for political corpora, see Roberts et al 2016,Nguyen et al 2013, Gerrish & Blei 2012and Lin et al 2008). …”

Section: Methodsmentioning

confidence: 99%

“…Based on the posterior draws of the topic indicators z and the framing indicators x we compute the posterior quantities of interest. The choice of the number of topics has been partly based on previous work by Quinn et al (2010), who analyzed a similar corpus and used 42 topics. Since our main interest lies in one seeded topic (immigration), the exact number of topics is less of a concern (see Appendix C.1.1 for a sensitivity analysis of the number of topics).…”

Section: Methodsmentioning

confidence: 99%

“…In Quinn et al (2010) the number of topics is varied and studied extensively. We wanted to study the effect of the total numbers of topics for the immigration topic.…”

Section: C11 the Number Of Topicsmentioning

confidence: 99%

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Voices from the far right: a text analysis of Swedish parliamentary debates

Magnusson¹,

Öhrvall²,

Barrling³

et al. 2018

Preprint

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In this paper we study the effects of a radical right party entering a national parliament, on the parliament discourse. We follow the classification developed by Meguid (2008) and use a probabilistic topic model approach to analyze the 300,000 speeches delivered in the Swedish parliament between 1994 and 2017. Our results indicate that immigration became a more prevalent topic in party leader debates when the Sweden Democrats entered the parliament in 2010. The other parties started to address immigration more, but still not to the extent that the Sweden Democrats did. In 2015, as Sweden faced a migration crisis, immigration became a more salient issue in the parliamentary debates. This could be seen as an external shock that forced the mainstream parties to put more emphasis on the topic of immigration. We conclude that the mainstream parties used a partly dismissive, partly adversarial strategy in their speeches when the SD entered the parliament. The migration crises in 2015 made them focus more on immigration and they thereby adopted a more adversarial strategy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In Quinn et al (2010) the number of topics is varied and studied extensively. We wanted to study the effect of the total numbers of topics for the immigration topic.…”

Section: C11 the Number Of Topicsmentioning

confidence: 99%

See 2 more Smart Citations

Voices from the far right: a text analysis of Swedish parliamentary debates

Magnusson¹,

Öhrvall²,

Barrling³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Unsupervised machine learning methods rely exclusively on data patterns to generate category membership (Grimmer, 2010;Quinn et al, 2010). They let the data do the talking.…”

Section: Introductionmentioning

confidence: 99%

Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods

Collingwood

Wilkerson

2012

Journal of Information Technology & Politics

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Text is becoming a central source of data for social science research. With advances in digitization and open records practices, the central challenge has in large part shifted away from availability to usability. Automated text classification methodologies are becoming increasingly important within political science because they hold the promise of substantially reducing the costs of converting text to data for a variety of tasks. In this paper, we consider a number of questions of interest to prospective users of supervised learning methods, which are appropriate to classification tasks where known categories are applied. For the right task, supervised learning methods can dramatically lower the costs associated with labeling large volumes of textual data while maintaining high reliability and accuracy. Information science researchers devote considerable attention to comparing the performance of supervised learning algorithms and different feature representations, but the questions posed are often less directly relevant to the practical concerns of social science researchers. The first question prospective social science users are likely to ask is -how well do such methods work? The second is likely to be -how much do they cost in terms of human labeling effort? Relatedly, how much do marginal improvements in performance cost? We address these questions in the context of a particular dataset -the Congressional Bills Project -which includes more than 400,000 labeled bill titles (19 policy topics). This corpus also provides opportunities to experiment with varying sample sizes and sampling methodologies. We are ultimately able to locate an accuracy/efficiency sweet spot of sorts for this dataset by leveraging results generated by an ensemble of supervised learning algorithms.

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Insight workflow: Systematically combining human and computational methods to explore textual data

Gill

Hinrichs

Blanke

et al. 2017

Asso for Info Science & Tech

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. Insight workflow: Systematically combining human and computational methods to explore textual data. Journal of the Association for Information Science and Technology, 68(7), 1671 -1686 . DOI: 10.1002 Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policyIf you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. AbstractAnalysing large quantities of real world textual data has the potential to provide new insights for researchers. However, such data present challenges for both human and computational methods, requiring a diverse range of specialist skills, often shared across a number of individuals. In this paper, we use the analysis of a real world data set as our case study, and use this exploration as a demonstration of our 'insight workflow', which we present for use and adaptation by other researchers. The data we use are impact case study documents collected as part of the UK Research Excellence Framework (REF), consisting of 6,679 documents and 6.25 million words; the analysis was commissioned by the Higher Education Funding Council for England (published as report HEFCE 2015). In our exploration and analysis we used a variety of techniques, ranging from keyword in context and frequency information to more sophisticated methods (topic modelling), with these automated techniques providing an empirical point of entry for in depth and intensive human analysis. We present the 60 topics to demonstrate the output of our methods, and illustrate how the variety of analysis techniques can be combined to provide insights. We note potential limitations and propose future work. SYSTEMATICALLY EXPLORING TEXT 3

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How to Analyze Political Attention with Minimal Assumptions and Costs

Cited by 524 publications

References 39 publications

Voices from the far right: a text analysis of Swedish parliamentary debates

Voices from the far right: a text analysis of Swedish parliamentary debates

Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods

Insight workflow: Systematically combining human and computational methods to explore textual data

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