Inference in an Authorship Problem

Mosteller, Frederick; Wallace, David

doi:10.1080/01621459.1963.10500849

Cited by 174 publications

(125 citation statements)

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“…However, the dependence stylistic attributes found for the proposed features suggests that attributes derived from the analysis of stylistic fluctuations can be combined in a hybrid way with traditional attributes, such as the frequency of function words [68]. As such, the findings reported in this paper shall potentially contribute to the improvement of current authorship recognition methods [67].…”

Section: Resultsmentioning

confidence: 99%

“…Simple approaches include the analysis of word length and additional character features [67]. Mosteller and Wallace [68] proved that the frequency of function words (such as 'and', 'any', 'ever', 'or', 'until' and 'with') can be employed to quantify the style of authors. More recently, many other approaches have been devised [58], including those relying upon topological analysis of complex networks [33,70].…”

Section: Resultsmentioning

confidence: 99%

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Authorship recognition via fluctuation analysis of network topology and word intermittency

Amancio¹

2015

J. Stat. Mech.

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Abstract. Statistical methods have been widely employed in many practicalnatural language processing applications. More specifically, complex network concepts and methods from dynamical systems theory have been successfully applied to recognize stylistic patterns in written texts. Despite the large number of studies devoted to representing texts with physical models, only a few studies have assessed the relevance of attributes derived from the analysis of stylistic fluctuations. Because fluctuations represent a pivotal factor for characterizing a myriad of real systems, this study focused on the analysis of the properties of stylistic fluctuations in texts via topological analysis of complex networks and intermittency measurements. The results showed that different authors display distinct fluctuation patterns. In particular, it was found that it is possible to identify the authorship of books using the intermittency of specific words. Taken together, the results described here suggest that the patterns found in stylistic fluctuations could be used to analyze other related complex systems. Furthermore, the discovery of novel patterns related to textual stylistic fluctuations indicates that these patterns could be useful to improve the state of the art of many stylistic-based natural language processing tasks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Authorship recognition via fluctuation analysis of network topology and word intermittency

Amancio¹

2015

J. Stat. Mech.

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“…Five such methods were cansidemed by Eisfarteds, Gilbert, and Avery [32]. A sixth method had been suggested by Mmteller and Wallace [35] and Joy and Tollefson [24]. Therefore the selEtion of methods which were used in this research study were based on their credibility among the model builders.…”

Section: Appendix E)mentioning

confidence: 99%

A methodology for predicting company failure in the construction industry

Abidali

1995

Construction Management and Economics

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“…Our paper also relates to other work on measuring document partisanship, including Laver et al (2003), Groseclose and Milyo (2005), Gentzkow and Shapiro (2010), Kim et al (2018), and Yan et al (2018). 3 Our paper contributes a recipe for using statistical predictability in a probability model of 3 More broadly, our paper relates to work in statistics on authorship determination (Mosteller and Wallace 1963), work in economics that uses text to measure the sentiment of a document (e.g., Antweiler and Frank 2004;Tetlock 2007), and work that classifies documents according to similarity of text (Blei and Lafferty 2007;Grimmer 2010). …”

Section: Introductionmentioning

confidence: 99%

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

Gentzkow¹,

Shapiro²,

Taddy

2016

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for their comments and suggestions. We thank Frances Lee for sharing her data on congressional communications staff. We also thank numerous seminar audiences and our many dedicated research assistants for their contributions to this project. This work was completed in part with resources provided by the University of Chicago Research Computing Center and the Stanford Research Computing Center. The data providers and funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w22423.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.© 2016 by Matthew Gentzkow, Jesse M. Shapiro, and Matt Taddy. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. ABSTRACTWe 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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Inference in an Authorship Problem

Cited by 174 publications

References 4 publications

Authorship recognition via fluctuation analysis of network topology and word intermittency

Authorship recognition via fluctuation analysis of network topology and word intermittency

A methodology for predicting company failure in the construction industry

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

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