Ngrams Against Agnotology: Combatting Tobacco Industry Narratives
About Addiction Through A Quantitative Analysis Of 14 Million Documents

Stephan Risi

risi@stanford.edu

Stanford University, United States of America

What happens when researchers have access to more documents than they could
read in a lifetime? As a result of litigation, historians of tobacco have
access to over 14 million formerly secret tobacco industry documents,
containing incriminating internal memos and research reports but also newspaper
clippings and consumer letters (UCSF Library and Center for Knowledge
Management). Historians have used this treasure trove to document widespread
fraud and systematic deception of smokers by the tobacco industry

(Brandt, 2007; Proctor, 2011). However, industry-friendly historians and
tobacco lawyers have started to rewrite some this history by claiming that
smokers always knew that smoking is addictive and causes cancer (Brandt, 2007;
Proctor, 2011). Usually, these claims rest on a few, well selected documents
that support a particular industry claim. Robert Proctor has called processes
like this “agnotology,” the cultural production of ignorance (Proctor, 2008).
Indeed, the arguments of both pro- and anti-tobacco industry historians rely on
the same corpus of data: an immense amount of publicly available and full-text
searchable documents. Given 14 million documents, there will be some supporting
almost any claim.

In this paper, I present one way to counter such ag-notological assertions by
studying broad trends across millions of documents with frequency analyses. In
particular, I counter the claim that smokers always knew that smoking was
addictive, an argument often made by tobacco lawyers in court to assign full
responsibility to the smoker (Henningfield, Rose, & Zeller, 2006) To refute
this assertion, I use frequency analyses with a validation measure to show that
smoking only became widely understood as an addiction in the late 1980s and
early 1990s, when scientists recognized that the same neural pathways were
involved in dependence to both nicotine and harder drugs like heroin and
cocaine. This inscription of addiction into the brain replaced older
explanations of why people smoke, like personality traits or an oral fixation.
Ultimately, I trace how the neurological understanding of nicotine addiction
moved from research laboratories to the public: it led to the Surgeon General's
warning labels; it enabled smokers to seek out new nicotine replacement
therapies; and it made it possible for smokers to successfully sue the tobacco
industry for the first time.

Frequency analysis, popularized by the Google Ngram Viewer, is one of the
simplest mathematical tools in the arsenal of the digital humanities (Michel
et al, 2011). By calculating the usage frequency of terms and expressions over
time, it enables users to get a sense of when a term became more or less
important. The mathematical simplicity confers it an important advantage: it
scales very well not just to thousands but to millions of documents. For this
study, I used all 14 million documents (about 10 billion tokens) dated between
1940 and 1998 to create a publicly available website (
www.tobacco-analytics.org), where users

can create their run their own frequency analyses, akin to Google ngrams.

[560-1]

Figure 1: Screenshot of the relative frequencies of "nicotine addiction" in the
tobacco documents from www.tobacco-analytics.org. The presentation will use a
number of these

graphs to show that smoking only became understood as an addiction in the late
1980s and early ‘90s.

The main drawback of this method is that the patterns found in the graphs of
the Google Ngram Viewer are hard to validate: Does a spike in a particular
year represent a statistically significant event or is it just a fluke? Is it
caused by 10 or 1000 documents? I address this problem in two ways: First, I
allow users to display the absolute number of appearances of a term by year to
give them a sense of the number of documents that cause a spike. Second, I am
developing a comparison statistic to calculate z-scores using the Corpus of
Historical American English (COHA) (Davies, 2010). By comparing frequencies
between the tobacco documents and the reference corpus (COHA), it allows me to
calculate when frequencies in the tobacco corpus deviate in a statistically
significant way (Darwin, 2008, p. 208-222). Given, for example, the above
graph of the relative frequencies of the term “nicotine addiction,” z-scores
can be used to show that the relative frequencies only started to deviate
significantly from the comparison corpus in the 1980s.

The tobacco documents provide us with an opportunity to think through the
problems that come with access to millions of secret documents. What if
millions of dollars in settlements hinge on historical arguments? What if there
are immense financial incentives to make false historical claims: to present
narratives that are borne out in a few well selected documents, but which
misrepresent the corpus as a whole? In the realm of tobacco, historical
arguments and knowledge circulate far outside of academia in courtrooms to sway
juries or in policy documents to change legislation. The immense size of the
tobacco documents archive makes it possible to find a few documents supporting
almost any claim. Findings from one group of documents can cancel out the
findings from other documents; statements by one expert discredit those
of another one. In these cases, quantitative analyses using the whole corpus
can be an arbiter of these claims. They are not sufficient to advance
historical arguments in themselves, but they can be used to test and disprove
hypotheses made on the basis of a smaller set of documents. The Tobacco
Analytics project makes powerful digital humanities tools available to
tobacco researchers who may not have a technical background, and it allows
historians to trace developments within the tobacco industry by examining the
whole corpus with the click of a mouse.

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Risks of Tobacco Use: A Review and Analysis of Experts Testifying on Behalf of
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Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., Pickett, J.
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