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A General Model of Author “Style” with Application to the UK House of Commons, 1935–2018

Published online by Cambridge University Press:  24 January 2020

Leslie Huang ,
Patrick O. Perry  and
Arthur Spirling Open the ORCID record for Arthur Spirling [Opens in a new window]
Leslie Huang*
Affiliation:
Graduate Student, Center for Data Science, New York University, 60 Fifth Avenue, New York, NY10011, USA. Email: [email protected]
Patrick O. Perry
Affiliation:
Senior Data Scientist, Oscar Health; Visiting Scholar, Center for Data Science, New York University, 60 Fifth Avenue, New York, NY10011, USA. Email: [email protected]
Arthur Spirling
Affiliation:
Professor of Politics and Data Science, New York University, 19 W4th St, New York, NY10012, USA. Email: [email protected]
*
*Email: [email protected]
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Abstract

We consider evidence for the assertion that backbench members of parliament (MPs) in the UK have become less distinctive from one another in terms of their speech. Noting that this claim has considerable normative and substantive implications, we review theory and findings in the area, which are ultimately ambiguous on this question. We then provide a new statistical model of distinctiveness that extends traditional efforts to statistically characterize the “style” of authors and apply it to a corpus of Hansard speeches from 1935 to 2018. In the aggregate, we find no evidence for the claim of more homogeneity. But this hides intriguing covariate effects: at the MP-level, panel regression results demonstrate that on average, more senior backbenchers tend to be less “different” in speech terms. We also show, however, that this pattern is changing: in recent times, it is more experienced MPs who speak most distinctively.

Keywords

House of Commonsstylometrytext as dataregression
Type
Articles
Information
Political Analysis , Volume 28 , Issue 3 , July 2020 , pp. 412 - 434
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Jeff Gill

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