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A corpus-based analysis of potential linguistic indicators of corporate deception in tobacco industry documents

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1 Stanford Prevention Research Center, Stanford School of Medicine, Stanford, California, United States
2 Center for Health & Risk Communication, University of Georgia, Athens, Georgia, United States
DOI
10.7287/peerj.preprints.1079v1
Published
Accepted
Subject Areas
Algorithms and Analysis of Algorithms, Computational Linguistics, Natural Language and Speech
Keywords
deception, verbs, tobacco, corporate fraud, function words, public health, linguistic analysis
Copyright
© 2015 Brown-Johnson et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Brown-Johnson C, Rubin D. 2015. A corpus-based analysis of potential linguistic indicators of corporate deception in tobacco industry documents. PeerJ PrePrints 3:e1079v1

Abstract

Introduction: To more fully understand the impact of specific language attributes on deception in corporate communication, we used a stratified random sample of tobacco industry documents to test a small number of individual potential automated linguistic indicators of corporate deception: cognitive-emotional verbs, allness and superlative terms, nonbinding verbs, and group mentality. Methods: Texts from the Tobacco Documents Corpus were categorized by audience addressed (industry internal/external) and company of origin, and compared for incidence of the deceptive language indicators. Results: Cognitive-emotional verbs were strongly associated with likely deception and most prevalent for external audiences and documents produced by the Tobacco Institute, an industry front group. Cognitive-emotional verbs include believe, think, seem, feel and realize, in opposition to action verbs (e.g. throw). Discussion: Linguistic measurements of cognitive-emotional verbs may help pinpoint deceptive and misleading corporate communication. Verb choice and the use of different classes of verbs may reflect corporate linguistic deception.

Author Comment

This is a submission to PeerJ Computer Science for review.

Additional Information

Competing Interests

The authors declare they have no competing interests.

Author Contributions

Cati Brown-Johnson conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.

Donald Rubin conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.

Data Deposition

The following information was supplied regarding the deposition of related data:

Tobacco documents are available in their entirety here: http://legacy.library.ucsf.edu/.

The Tobacco Documents Corpus (a subset of the full tobacco documents library) is available here: http://www.tobaccodocs.uga.edu/.

Funding

This work was supported by the National Institute of Mental Health, Bethesda, Maryland, grant number #R01 MH083684 (PI Judith J. Prochaska); National Cancer Institute, Bethesda, Maryland, grants number CA-113710, R01 CA-87472 (PI Judith J. Prochaska), and R01 CA87490 (PI Donald L Rubin); National Institute on Drug Abuse, Bethesda, Maryland, grants number #K23 DA018691 and #P50 DA09253 (PI Judith J. Prochaska); NIH National Heart, Lung and Blood Institute, Bethesda, Maryland, grant #5T32 HL007034-39 (PI Christopher Gardner); and the State of California Tobacco-Related Disease Research Program, University of California Office of the President, Oakland, California grant number #21BT-0018 (PI Judith J. Prochaska). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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A corpus-based analysis of potential linguistic indicators of corporate deception in tobacco industry documents
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