Quantitative Analysis of Culture Using Millions of Digitized Books
Jean-Baptiste Michel, 1,2,3,4 *† Yuan Kui Shen, 5 Aviva Presser Aiden, 6 Adrian Veres, 7 Matthew K. Gray, 8 The Google Books
Team, 8 Joseph P. Pickett, 9 Dale Hoiberg, 10 Dan Clancy, 8 Peter Norvig, 8 Jon Orwant, 8 Steven Pinker, 4 Martin A. Nowak, 1,11,12
Erez Lieberman Aiden 1,12,13,14,15,16 *†
1 Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA. 2 Institute for Quantitative Social
Sciences, Harvard University, Cambridge, MA 02138, USA. 3 Department of Psychology, Harvard University, Cambridge, MA
02138, USA. 4 Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. 5 Computer Science and
Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA. 6 Harvard Medical School, Boston, MA, 02115, USA.
7 Harvard College, Cambridge, MA 02138, USA. 8 Google, Inc., Mountain View, CA, 94043, USA. 9 Houghton Mifflin Harcourt,
Boston, MA 02116, USA. 10 Encyclopaedia Britannica, Inc., Chicago, IL 60654, USA. 11 Dept of Organismic and Evolutionary
Biology, Harvard University, Cambridge, MA 02138, USA. 12 Dept of Mathematics, Harvard University, Cambridge, MA
02138, USA. 13 Broad Institute of Harvard and MIT, Harvard University, Cambridge, MA 02138, USA. 14 School of Engineering
and Applied Sciences, Harvard University, Cambridge, MA 02138, USA. 15 Harvard Society of Fellows, Harvard University,
Cambridge, MA 02138, USA. 16 Laboratory-at-Large, Harvard University, Cambridge, MA 02138, USA.
*These authors contributed equally to this work.
†To whom correspondence should be addressed. E-mail: jb.michel@gmail.com (J.B.M.); erez@erez.com (E.A.).
We constructed a corpus of digitized texts containing
about 4% of all books ever printed. Analysis of this
corpus enables us to investigate cultural trends
quantitatively. We survey the vast terrain of
“culturomics”, focusing on linguistic and cultural
phenomena that were reflected in the English language
between 1800 and 2000. We show how this approach can
provide insights about fields as diverse as lexicography,
the evolution of grammar, collective memory, the
adoption of technology, the pursuit of fame, censorship,
and historical epidemiology. “Culturomics” extends the
boundaries of rigorous quantitative inquiry to a wide
array of new phenomena spanning the social sciences and
the humanities.
Reading small collections of carefully chosen works enables
scholars to make powerful inferences about trends in human
thought. However, this approach rarely enables precise
measurement of the underlying phenomena. Attempts to
introduce quantitative methods into the study of culture (1-6)
have been hampered by the lack of suitable data.
We report the creation of a corpus of 5,195,769 digitized
books containing ~4% of all books ever published.
Computational analysis of this corpus enables us to observe
cultural trends and subject them to quantitative investigation.
“Culturomics” extends the boundaries of scientific inquiry to
a wide array of new phenomena.
The corpus has emerged from Google’s effort to digitize
books. Most books were drawn from over 40 university
libraries around the world. Each page was scanned with
custom equipment (7), and the text digitized using optical
character recognition (OCR). Additional volumes – both
physical and digital – were contributed by publishers.
Metadata describing date and place of publication were
provided by the libraries and publishers, and supplemented
with bibliographic databases. Over 15 million books have
been digitized [12% of all books ever published (7)]. We
selected a subset of over 5 million books for analysis on the
basis of the quality of their OCR and metadata (Fig. 1A) (7).
Periodicals were excluded.
The resulting corpus contains over 500 billion words, in
English (361 billion), French (45B), Spanish (45B), German
(37B), Chinese (13B), Russian (35B), and Hebrew (2B). The
oldest works were published in the 1500s. The early decades
are represented by only a few books per year, comprising
several hundred thousand words. By 1800, the corpus grows
to 60 million words per year; by 1900, 1.4 billion; and by
2000, 8 billion.
The corpus cannot be read by a human. If you tried to read
only the entries from the year 2000 alone, at the reasonable
pace of 200 words/minute, without interruptions for food or
sleep, it would take eighty years. The sequence of letters is
one thousand times longer than the human genome: if you
wrote it out in a straight line, it would reach to the moon and
back 10 times over (8).
To make release of the data possible in light of copyright
constraints, we restricted our study to the question of how
often a given “1-gram” or “n-gram” was used over time. A 1-
gram is a string of characters uninterrupted by a space; this
includes words (“banana”, “SCUBA”) but also numbers
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(“3.14159”) and typos (“excesss”). An n-gram is sequence of
1-grams, such as the phrases “stock market” (a 2-gram) and
“the United States of America” (a 5-gram). We restricted n to
5, and limited our study to n-grams occurring at least 40 times
in the corpus.
Usage frequency is computed by dividing the number of
instances of the n-gram in a given year by the total number of
words in the corpus in that year. For instance, in 1861, the 1-
gram “slavery” appeared in the corpus 21,460 times, on
11,687 pages of 1,208 books. The corpus contains
386,434,758 words from 1861; thus the frequency is 5.5x10 -5 .
“slavery” peaked during the civil war (early 1860s) and then
again during the civil rights movement (1955-1968) (Fig. 1B)
In contrast, we compare the frequency of “the Great War”
to the frequencies of “World War I” and “World War II.” “the
Great War” peaks between 1915 and 1941. But although its
frequency drops thereafter, interest in the underlying events
had not disappeared; instead, they are referred to as “World
War I” (Fig. 1C).
These examples highlight two central factors that
contribute to culturomic trends. Cultural change guides the
concepts we discuss (such as “slavery”). Linguistic change –
which, of course, has cultural roots – affects the words we use
for those concepts (“the Great War” vs. “World War I”). In
this paper, we will examine both linguistic changes, such as
changes in the lexicon and grammar; and cultural phenomena,
such as how we remember people and events.
The full dataset, which comprises over two billion
culturomic trajectories, is available for download or
exploration at www.culturomics.org.
The Size of the English Lexicon
How many words are in the English language (9)?
We call a 1-gram “common” if its frequency is greater
than one per billion. (This corresponds to the frequency of the
words listed in leading dictionaries (7).) We compiled a list of
all common 1-grams in 1900, 1950, and 2000 based on the
frequency of each 1-gram in the preceding decade. These lists
contained 1,117,997 common 1-grams in 1900, 1,102,920 in
1950, and 1,489,337 in 2000.
Not all common 1-grams are English words. Many fell
into three non-word categories: (i) 1-grams with nonalphabetic
characters (“l8r”, “3.14159”); (ii) misspellings
(“becuase, “abberation”); and (iii) foreign words
(“sensitivo”).
To estimate the number of English words, we manually
annotated random samples from the lists of common 1-grams
(7) and determined what fraction were members of the above
non-word categories. The result ranged from 51% of all
common 1-grams in 1900 to 31% in 2000.
Using this technique, we estimated the number of words in
the English lexicon as 544,000 in 1900, 597,000 in 1950, and
1,022,000 in 2000. The lexicon is enjoying a period of
enormous growth: the addition of ~8500 words/year has
increased the size of the language by over 70% during the last
fifty years (Fig. 2A).
Notably, we found more words than appear in any
dictionary. For instance, the 2002 Webster’s Third New
International Dictionary [W3], which keeps track of the
contemporary American lexicon, lists approximately 348,000
single-word wordforms (10); the American Heritage
Dictionary of the English Language, Fourth Edition (AHD4)
lists 116,161 (11). (Both contain additional multi-word
entries.) Part of this gap is because dictionaries often exclude
proper nouns and compound words (“whalewatching”). Even
accounting for these factors, we found many undocumented
words, such as “aridification” (the process by which a
geographic region becomes dry), “slenthem” (a musical
instrument), and, appropriately, the word “deletable.”
This gap between dictionaries and the lexicon results from
a balance that every dictionary must strike: it must be
comprehensive enough to be a useful reference, but concise
enough to be printed, shipped, and used. As such, many
infrequent words are omitted. To gauge how well dictionaries
reflect the lexicon, we ordered our year 2000 lexicon by
frequency, divided it into eight deciles (ranging from 10 -9 –
10 -8 to 10 -2 – 10 -1 ), and sampled each decile (7). We manually
checked how many sample words were listed in the OED (12)
and in the Merriam-Webster Unabridged Dictionary [MWD].
(We excluded proper nouns, since neither OED nor MWD
lists them.) Both dictionaries had excellent coverage of high
frequency words, but less coverage for frequencies below 10 -
6 : 67% of words in the 10 -9 – 10 -8 range were listed in neither
dictionary (Fig. 2B). Consistent with Zipf’s famous law, a
large fraction of the words in our lexicon (63%) were in this
lowest frequency bin. As a result, we estimated that 52% of
the English lexicon – the majority of the words used in
English books – consists of lexical “dark matter”
undocumented in standard references (12).
To keep up with the lexicon, dictionaries are updated
regularly (13). We examined how well these changes
corresponded with changes in actual usage by studying the
2077 1-gram headwords added to AHD4 in 2000. The overall
frequency of these words, such as “buckyball” and
“netiquette”, has soared since 1950: two-thirds exhibited
recent, sharp increases in frequency (>2X from 1950-2000)
(Fig. 2C). Nevertheless, there was a lag between
lexicographers and the lexicon. Over half the words added to
AHD4 were part of the English lexicon a century ago
(frequency >10 -9 from 1890-1900). In fact, some newlyadded
words, such as “gypseous” and “amplidyne”, have
already undergone a steep decline in frequency (Fig. 2D).
Not only must lexicographers avoid adding words that
have fallen out of fashion, they must also weed obsolete
words from earlier editions. This is an imperfect process. We
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found 2220 obsolete 1-gram headwords (“diestock”,
“alkalescent”) in AHD4. Their mean frequency declined
throughout the 20th century, and dipped below 10 -9 decades
ago (Fig. 2D, Inset).
Our results suggest that culturomic tools will aid
lexicographers in at least two ways: (i) finding low-frequency
words that they do not list; and (ii) providing accurate
estimates of current frequency trends to reduce the lag
between changes in the lexicon and changes in the dictionary.
The Evolution of Grammar
Next, we examined grammatical trends. We studied the
English irregular verbs, a classic model of grammatical
change (14-17). Unlike regular verbs, whose past tense is
generated by adding –ed (jump/jumped), irregulars are
conjugated idiosyncratically (stick/stuck, come/came, get/got)
(15).
All irregular verbs coexist with regular competitors (e.g.,
“strived” and “strove”) that threaten to supplant them (Fig.
2E). High-frequency irregulars, which are more readily
remembered, hold their ground better. For instance, we found
“found” (frequency: 5x10 -4 ) 200,000 times more often than
we finded “finded.” In contrast, “dwelt” (frequency: 1x10 -5 )
dwelt in our data only 60 times as often as “dwelled” dwelled.
We defined a verb’s “regularity” as the percentage of
instances in the past tense (i.e., the sum of “drived”, “drove”,
and “driven”) in which the regular form is used. Most
irregulars have been stable for the last 200 years, but 16%
underwent a change in regularity of 10% or more (Fig. 2F).
These changes occurred slowly: it took 200 years for our
fastest moving verb, “chide”, to go from 10% to 90%.
Otherwise, each trajectory was sui generis; we observed no
characteristic shape. For instance, a few verbs, like “spill”,
regularized at a constant speed, but others, such as “thrive”
and “dig”, transitioned in fits and starts (7). In some cases, the
trajectory suggested a reason for the trend. For example, with
“sped/speeded” the shift in meaning from “to move rapidly”
and towards “to exceed the legal limit” appears to have been
the driving cause (Fig. 2G).
Six verbs (burn, chide, smell, spell, spill, thrive)
regularized between 1800 and 2000 (Fig. 2F). Four are
remnants of a now-defunct phonological process that used –t
instead of –ed; they are members of a pack of irregulars that
survived by virtue of similarity (bend/bent, build/built,
burn/burnt, learn/learnt, lend/lent, rend/rent, send/sent,
smell/smelt, spell/spelt, spill/spilt, and spoil/spoilt). Verbs
have been defecting from this coalition for centuries
(wend/went, pen/pent, gird/girt, geld/gelt, and gild/gilt all
blend/blent into the dominant –ed rule). Culturomic analysis
reveals that the collapse of this alliance has been the most
significant driver of regularization in the past 200 years. The
regularization of burnt, smelt, spelt, and spilt originated in the
US; the forms still cling to life in British English (Fig. 2E,F).
But the –t irregulars may be doomed in England too: each
year, a population the size of Cambridge adopts “burned” in
lieu of “burnt.”
Though irregulars generally yield to regulars, two verbs
did the opposite: light/lit and wake/woke. Both were irregular
in Middle English, were mostly regular by 1800, and
subsequently backtracked and are irregular again today. The
fact that these verbs have been going back and forth for
nearly 500 years highlights the gradual nature of the
underlying process.
Still, there was at least one instance of rapid progress by
an irregular form. Presently, 1% of the English speaking
population switches from “sneaked” to “snuck” every year:
someone will have snuck off while you read this sentence. As
before, this trend is more prominent in the United States, but
recently sneaked across the Atlantic: America is the world’s
leading exporter of both regular and irregular verbs.
Out with the Old
Just as individuals forget the past (18, 19), so do societies
(20). To quantify this effect, we reasoned that the frequency
of 1-grams such as “1951” could be used to measure interest
in the events of the corresponding year, and created plots for
each year between 1875 and 1975.
The plots had a characteristic shape. For example, “1951”
was rarely discussed until the years immediately preceding
1951. Its frequency soared in 1951, remained high for three
years, and then underwent a rapid decay, dropping by half
over the next fifteen years. Finally, the plots enter a regime
marked by slower forgetting: collective memory has both a
short-term and a long-term component.
But there have been changes. The amplitude of the plots is
rising every year: precise dates are increasingly common.
There is also a greater focus on the present. For instance,
“1880” declined to half its peak value in 1912, a lag of 32
years. In contrast, “1973” declined to half its peak by 1983, a
lag of only 10 years. We are forgetting our past faster with
each passing year (Fig. 3A).
We were curious whether our increasing tendency to forget
the old was accompanied by more rapid assimilation of the
new (21). We divided a list of 154 inventions into timeresolved
cohorts based on the forty-year interval in which
they were first invented (1800-1840, 1840-1880, and 1880-
1920) (7). We tracked the frequency of each invention in the
nth after it was invented as compared to its maximum value,
and plotted the median of these rescaled trajectories for each
cohort.
The inventions from the earliest cohort (1800-1840) took
over 66 years from invention to widespread impact
(frequency >25% of peak). Since then, the cultural adoption
of technology has become more rapid: the 1840-1880
invention cohort was widely adopted within 50 years; the
1880-1920 cohort within 27 (Fig. 3B).
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“In the Future, Everyone Will Be World Famous for 7.5
Minutes” –Whatshisname
People, too, rise to prominence, only to be forgotten (22).
Fame can be tracked by measuring the frequency of a
person’s name (Fig. 3C). We compared the rise to fame of the
most famous people of different eras. We took all 740,000
people with entries in Wikipedia, removed cases where
several famous individuals share a name, and sorted the rest
by birthdate and frequency (23). For every year from 1800-
1950, we constructed a cohort consisting of the fifty most
famous people born in that year. For example, the 1882
cohort includes “Virginia Woolf” and “Felix Frankfurter”; the
1946 cohort includes “Bill Clinton” and “Steven Spielberg.”
We plotted the median frequency for the names in each
cohort over time (Fig. 3D-E). The resulting trajectories were
all similar. Each cohort had a pre-celebrity period ( median
frequency <10 -9 ), followed by a rapid rise to prominence, a
peak, and a slow decline. We therefore characterized each
cohort using four parameters: (i) the age of initial celebrity;
(ii) the doubling time of the initial rise; (iii) the age of peak
celebrity; (iv) the half-life of the decline (Fig. 3E). The age of
peak celebrity has been consistent over time: about 75 years
after birth. But the other parameters have been changing.
Fame comes sooner and rises faster: between the early 19th
century and the mid-20th century, the age of initial celebrity
declined from 43 to 29 years, and the doubling time fell from
8.1 to 3.3 years. As a result, the most famous people alive
today are more famous – in books – than their predecessors.
Yet this fame is increasingly short-lived: the post-peak halflife
dropped from 120 to 71 years during the nineteenth
century.
We repeated this analysis with all 42,358 people in the
databases of Encyclopaedia Britannica (24), which reflect a
process of expert curation that began in 1768. The results
were similar (7). Thus, people are getting more famous than
ever before, but are being forgotten more rapidly than ever.
Occupational choices affect the rise to fame. We focused
on the 25 most famous individuals born between 1800 and
1920 in seven occupations (actors, artists, writers, politicians,
biologists, physicists, and mathematicians), examining how
their fame grew as a function of age (Fig. 3F).
Actors tend to become famous earliest, at around 30. But
the fame of the actors we studied – whose ascent preceded the
spread of television – rises slowly thereafter. (Their fame
peaked at a frequency of 2x10 -7 .) The writers became famous
about a decade after the actors, but rose for longer and to a
much higher peak (8x10 -7 ). Politicians did not become
famous until their 50s, when, upon being elected President of
the United States (in 11 of 25 cases; 9 more were heads of
other states) they rapidly rose to become the most famous of
the groups (1x10 -6 ).
Science is a poor route to fame. Physicists and biologists
eventually reached a similar level of fame as actors (1x10 -7 ),
but it took them far longer. Alas, even at their peak,
mathematicians tend not to be appreciated by the public
(2x10 -8 ).
Detecting Censorship and Suppression
Suppression – of a person, or an idea – leaves quantifiable
fingerprints (25). For instance, Nazi censorship of the Jewish
artist Marc Chagall is evident by comparing the frequency of
“Marc Chagall” in English and in German books (Fig.4A). In
both languages, there is a rapid ascent starting in the late
1910s (when Chagall was in his early 30s). In English, the
ascent continues. But in German, the artist’s popularity
decreases, reaching a nadir from 1936-1944, when his full
name appears only once. (In contrast, from 1946-1954, “Marc
Chagall” appears nearly 100 times in the German corpus.)
Such examples are found in many countries, including Russia
(e.g. Trotsky), China (Tiananmen Square) and the US (the
Hollywood Ten, blacklisted in 1947) (Fig.4B-D).
We probed the impact of censorship on a person’s cultural
influence in Nazi Germany. Led by such figures as the
librarian Wolfgang Hermann, the Nazis created lists of
authors and artists whose “undesirable”, “degenerate” work
was banned from libraries and museums and publicly burned
(26-28). We plotted median usage in German for five such
lists: artists (100 names), as well as writers of Literature
(147), Politics (117), History (53), and Philosophy (35) (Fig
4E). We also included a collection of Nazi party members
[547 names, ref (7)]. The five suppressed groups exhibited a
decline. This decline was modest for writers of history (9%)
and literature (27%), but pronounced in politics (60%),
philosophy (76%), and art (56%). The only group whose
signal increased during the Third Reich was the Nazi party
members [a 500% increase; ref (7)].
Given such strong signals, we tested whether one could
identify victims of Nazi repression de novo. We computed a
“suppression index” s for each person by dividing their
frequency from 1933 – 1945 by the mean frequency in 1925-
1933 and in 1955-1965 (Fig.4F, Inset). In English, the
distribution of suppression indices is tightly centered around
unity. Fewer than 1% of individuals lie at the extremes (s<1/5
or s>5).
In German, the distribution in much wider, and skewed
leftward: suppression in Nazi Germany was not the
exception, but the rule (Fig. 4F). At the far left, 9.8% of
individuals showed strong suppression (s<1/5). This
population is highly enriched for documented victims of
repression, such as Pablo Picasso (s=0.12), the Bauhaus
architect Walter Gropius (s=0.16), and Hermann Maas
(s<.01), an influential Protestant Minister who helped many
Jews flee (7). (Maas was later recognized by Israel’s Yad
Vashem as a “Righteous Among the Nations.”) At the other
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extreme, 1.5% of the population exhibited a dramatic rise
(s>5). This subpopulation is highly enriched for Nazis and
Nazi-supporters, who benefited immensely from government
propaganda (7).
These results provide a strategy for rapidly identifying
likely victims of censorship from a large pool of possibilities,
and highlights how culturomic methods might complement
existing historical approaches.
Culturomics
Culturomics is the application of high-throughput data
collection and analysis to the study of human culture. Books
are a beginning, but we must also incorporate newspapers
(29), manuscripts (30), maps (31), artwork (32), and a myriad
of other human creations (33, 34). Of course, many voices –
already lost to time – lie forever beyond our reach.
Culturomic results are a new type of evidence in the
humanities. As with fossils of ancient creatures, the challenge
of culturomics lies in the interpretation of this evidence.
Considerations of space restrict us to the briefest of surveys: a
handful of trajectories and our initial interpretations. Many
more fossils, with shapes no less intriguing, beckon:
(i) Peaks in “influenza” correspond with dates of known
pandemics, suggesting the value of culturomic methods for
historical epidemiology (35) (Fig. 5A).
(ii) Trajectories for “the North”, “the South”, and finally,
“the enemy” reflect how polarization of the states preceded
the descent into war (Fig. 5B).
(iii) In the battle of the sexes, the “women” are gaining
ground on the “men” (Fig. 5C).
(iv) “féminisme” made early inroads in France, but the US
proved to be a more fertile environment in the long run (Fig.
5D).
(v) “Galileo”, “Darwin”, and “Einstein” may be well-known
scientists, but “Freud” is more deeply engrained in our
collective subconscious (Fig. 5E).
(vi) Interest in “evolution” was waning when “DNA”
came along (Fig. 5F).
(vii) The history of the American diet offers many
appetizing opportunities for future research; the menu
includes “steak”, “sausage”, “ice cream”, “hamburger”,
“pizza”, “pasta”, and “sushi” (Fig. 5G).
(viii) “God” is not dead; but needs a new publicist (Fig.
5H).
These, together with the billions of other trajectories that
accompany them, will furnish a great cache of bones from
which to reconstruct the skeleton of a new science.
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36. J-B.M. was supported by the Foundational Questions in
Evolutionary Biology Prize Fellowship and the Systems
Biology Program (Harvard Medical School). Y.K.S. was
supported by internships at Google. S.P. acknowledges
support from NIH grant HD 18381. E.A. was supported by
the Harvard Society of Fellows, the Fannie and John Hertz
Foundation Graduate Fellowship, the National Defense
Science and Engineering Graduate Fellowship, the NSF
Graduate Fellowship, the National Space Biomedical
Research Institute, and NHGRI Grant T32 HG002295 .
This work was supported by a Google Research Award.
The Program for Evolutionary Dynamics acknowledges
support from the Templeton Foundation, NIH grant
R01GM078986, and the Bill and Melinda Gates
Foundation. Some of the methods described in this paper
are covered by US patents 7463772 and 7508978. We are
grateful to D. Bloomberg, A. Popat, M. McCormick, T.
Mitchison, U. Alon, S. Shieber, E. Lander, R. Nagpal, J.
Fruchter, J. Guldi, J. Cauz, C. Cole, P. Bordalo, N.
Christakis, C. Rosenberg, M. Liberman, J. Sheidlower, B.
Zimmer, R. Darnton, and A. Spector for discussions; to C-
M. Hetrea and K. Sen for assistance with Encyclopaedia
Britannica's database, to S. Eismann, W. Treß, and the
City of Berlin website (berlin.de) for assistance
documenting victims of Nazi censorship, to C. Lazell and
G.T. Fournier for assistance with annotation, to M. Lopez
for assistance with Fig. 1, to G. Elbaz and W. Gilbert for
reviewing an early draft, and to Google’s library partners
and every author who has ever picked up a pen, for books.
Supporting Online Material
www.sciencemag.org/cgi/content/full/science.1199644/DC1
Materials and Methods
Figs. S1 to S19
References
27 October 2010; accepted 6 December 2010
Published online 16 December 2010;
10.1126/science.1199644
Fig. 1. “Culturomic” analyses study millions of books at
once. (A) Top row: authors have been writing for millennia;
~129 million book editions have been published since the
advent of the printing press (upper left). Second row:
Libraries and publishing houses provide books to Google for
scanning (middle left). Over 15 million books have been
digitized. Third row: each book is associated with metadata.
Five million books are chosen for computational analysis
(bottom left). Bottom row: a culturomic “timeline” shows the
frequency of “apple” in English books over time (1800-
2000). (B) Usage frequency of “slavery.” The Civil War
(1861-1865) and the civil rights movement (1955-1968) are
highlighted in red. The number in the upper left (1e-4) is the
unit of frequency. (C) Usage frequency over time for “the
Great War” (blue), “World War I” (green), and “World War
II” (red).
Fig. 2. Culturomics has profound consequences for the study
of language, lexicography, and grammar. (A) The size of the
English lexicon over time. Tick marks show the number of
single words in three dictionaries (see text). (B) Fraction of
words in the lexicon that appear in two different dictionaries
as a function of usage frequency. (C) Five words added by
the AHD in its 2000 update. Inset: Median frequency of new
words added to AHD4 in 2000. The frequency of half of these
words exceeded 10 -9 as far back as 1890 (white dot). (D)
Obsolete words added to AHD4 in 2000. Inset: Mean
frequency of the 2220 AHD headwords whose current usage
frequency is less than 10 -9 . (E) Usage frequency of irregular
verbs (red) and their regular counterparts (blue). Some verbs
(chide/chided) have regularized during the last two centuries.
The trajectories for “speeded” and “speed up” (green) are
similar, reflecting the role of semantic factors in this instance
of regularization. The verb “burn” first regularized in the US
(US flag) and later in the UK (UK flag). The irregular
“snuck” is rapidly gaining on “sneaked.” (F) Scatter plot of
the irregular verbs; each verb’s position depends on its
regularity (see text) in the early 19th century (x-coordinate)
and in the late 20th century (y-coordinate). For 16% of the
verbs, the change in regularity was greater than 10% (large
font). Dashed lines separate irregular verbs (regularity<50%)
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from regular verbs (regularity>50%). Six verbs became
regular (upper left quadrant, blue), while two became
irregular (lower right quadrant, red). Inset: the regularity of
“chide” over time. (G) Median regularity of verbs whose past
tense is often signified with a –t suffix instead of –ed (burn,
smell, spell, spill, dwell, learn, and spoil) in US (black) and
UK (grey) books.
Fig. 3. Cultural turnover is accelerating. (A) We forget:
frequency of 1883 (blue), 1910 (green) and 1950 (red). Inset:
We forget faster. The half-life of the curves (grey dots) is
getting shorter (grey line: moving average). (B) Cultural
adoption occurs faster. Median trajectory for three cohorts of
inventions from three different time periods (1800-1840:
blue, 1840-1880: green, 1880-1920: red). Inset: The
telephone (green, date of invention: green arrow) and radio
(blue, date of invention: blue arrow). (C) Fame of various
personalities born between 1920 and 1930. (D) Frequency of
the 50 most famous people born in 1871 (grey lines; median:
dark gray). Five examples are highlighted. (E) The median
trajectory of the 1865 cohort is characterized by four
parameters: (i) initial “age of celebrity” (34 years old, tick
mark); (ii) doubling time of the subsequent rise to fame (4
years, blue line); (iii) “age of peak celebrity” (70 years after
birth, tick mark), and (iv) half-life of the post-peak
“forgetting” phase (73 years, red line). Inset: The doubling
time and half-life over time. (F) The median trajectory of the
25 most famous personalities born between 1800 and 1920 in
various careers.
Fig. 4. Culturomics can be used to detect censorship. (A)
Usage frequency of “Marc Chagall” in German (red) as
compared to English (blue). (B) Suppression of Leon Trotsky
(blue), Grigory Zinoviev (green), and Lev Kamenev (red) in
Russian texts, with noteworthy events indicated: Trotsky’s
assassination (blue arrow), Zinoviev and Kamenev executed
(red arrow), the “Great Purge” (red highlight), perestroika
(grey arrow). (C) The 1976 and 1989 Tiananmen Square
incidents both lead to elevated discussion in English texts.
Response to the 1989 incident is largely absent in Chinese
texts (blue), suggesting government censorship. (D) After the
“Hollywood Ten” were blacklisted (red highlight) from
American movie studios, their fame declined (median: wide
grey). None of them were credited in a film until 1960’s
(aptly named) “Exodus.” (E) Writers in various disciplines
were suppressed by the Nazi regime (red highlight). In
contrast, the Nazis themselves (thick red) exhibited a strong
fame peak during the war years. (F) Distribution of
suppression indices for both English (blue) and German (red)
for the period from 1933-1945. Three victims of Nazi
suppression are highlighted at left (red arrows). Inset:
Calculation of the suppression index for “Henri Matisse.”
Fig. 5. Culturomics provides quantitative evidence for
scholars in many fields. (A) Historical Epidemiology:
“influenza” is shown in blue; the Russian, Spanish, and Asian
flu epidemics are highlighted. (B) History of the Civil War.
(C) Comparative History. (D) Gender studies. (E and F)
History of Science. (G) Historical Gastronomy. (H) History
of Religion: “God.”
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www.sciencemag.org/cgi/content/full/science.1199644/DC1
Supporting Online Material for
Quantitative Analysis of Culture Using Millions of Digitized Books
Jean-Baptiste Michel,* Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K.
Gray, The Google Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter
Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, Erez Lieberman Aiden*
*To whom correspondence should be addressed. E-mail: jb.michel@gmail.com (J.B.M.); erez@erez.com
(E.A.).
This PDF file includes:
Materials and Methods
Figs. S1 to S19
References
Published 16 December 2010 on Science Express
DOI: 10.1126/science.1199644
Materials and Methods
“Quantitative analysis of culture using millions of digitized books”,
Michel et al.
Contents
I. Overview of Google Books Digitization ......................................................................................... 3
I.1. Metadata ....................................................................................................................... 3
I.2. Digitization ..................................................................................................................... 4
I.3. Structure Extraction ...................................................................................................... 4
II. Construction of Historical N-grams Corpora ................................................................................ 5
II.1. Additional filtering of books .......................................................................................... 5
II.1A. Accuracy of Date-of-Publication metadata ................................................................ 5
II.1B. OCR quality ............................................................................................................... 6
II.1C. Accuracy of language metadata ................................................................................ 6
II.1D. Year Restriction ......................................................................................................... 7
II.2. Metadata based subdivision of the Google Books Collection ...................................... 7
II.2A. Determination of language ........................................................................................ 7
II.2B. Determination of book subject assignments .............................................................. 7
II.2C. Determination of book country-of-publication ............................................................ 7
II.3. Construction of historical n-grams corpora .................................................................. 8
II.3A. Creation of a digital sequence of 1-grams and extraction of n-gram counts ............. 8
II.3B. Generation of historical n-grams corpora ................................................................ 10
III. Culturomic Analyses .............................................................................................................................. 12
III.0. General Remarks ................................................................................................................... 12
III.0.1 On Corpora. ......................................................................................................................... 12
III.0.2 On the number of books published ...................................................................................... 13
III.1. Generation of timeline plots ................................................................................................... 13
III.1A. Single Query ........................................................................................................................ 13
III.1B. Multiple Query/Cohort Timelines ......................................................................................... 14
III.2. Note on collection of historical and cultural data ................................................................... 14
III.3. Controls .................................................................................................................................. 15
III.4. Lexicon Analysis .................................................................................................................... 15
1
III.4A. Estimation of the number of 1-grams defined in leading dictionaries of the English
language. ....................................................................................................................................... 15
III.4B. Estimation of Lexicon Size .................................................................................................. 16
III.4C. Dictionary Coverage ............................................................................................................ 17
III.4D. Analysis New and Obsolete words in the American Heritage Dictionary ............................ 17
III.5. The Evolution of Grammar ..................................................................................................... 17
III.5A. Ensemble of verbs studied .................................................................................................. 17
III.5B. Verb frequencies.................................................................................................................. 18
III.5C. Rates of regularization ........................................................................................................ 18
III.5D. Classification of Verbs ......................................................................................................... 18
III.6. Collective Memory.................................................................................................................. 18
III.7. The Pursuit of Fame............................................................................................................... 19
III.7A) Complete procedure ............................................................................................................ 19
III.7B. Cohorts of fame ................................................................................................................... 25
III.8. History of Technology ............................................................................................................ 26
III.9. Censorship ............................................................................................................................. 26
III.9A. Comparing the influence of censorship and propaganda on various groups ...................... 26
III.9B. De Novo Identification of Censored and Suppressed Individuals ....................................... 28
III.9C. Validation by an expert annotator........................................................................................ 28
III.10. Epidemics ............................................................................................................................. 29
2
I. Overview of Google Books Digitization
In 2004, Google began scanning books to make their contents searchable and discoverable online. To
date, Google has scanned over fifteen million books: over 11% of all the books ever published. The
collection contains over five billion pages and two trillion words, with books dating back to as early as
1473 and with text in 478 languages. Over two million of these scanned books were given directly to
Google by their publishers; the rest are borrowed from large libraries such as the University of Michigan
and the New York Public Library. The scanning effort involves significant engineering challenges, some of
which are highly relevant to the construction of the historical n-grams corpus. We survey those issues
here.
The result of the next three steps is a collection of digital texts associated with particular book editions, as
well as composite metadata for each edition combining the information contained in all metadata sources.
I.1. Metadata
Over 100 sources of metadata information were used by Google to generate a comprehensive catalog of
books. Some of these sources are library catalogs (e.g., the list of books in the collections of University of
Michigan, or union catalogs such as the collective list of books in Bosnian libraries), some are from
retailers (e.g., Decitre, a French bookseller), and some are from commercial aggregators (e.g., Ingram).
In addition, Google also receives metadata from its 30,000 partner publishers. Each metadata source
consists of a series of digital records, typically in either the MARC format favored by libraries, or the ONIX
format used by the publishing industry. Each record refers to either a specific edition of a book or a
physical copy of a book on a library shelf, and contains conventional bibliographic data such as title,
author(s), publisher, date of publication, and language(s) of publication.
Cataloguing practices vary widely among these sources, and even within a single source over time. Thus
two records for the same edition will often differ in multiple fields. This is especially true for serials (e.g.,
the Congressional Record) and multivolume works such as sets (e.g., the three volumes of The Lord of
the Rings).
The matter is further complicated by ambiguities in the definition of the word „book‟ itself. Including
translations, there are over three thousand editions derived from Mark Twain‟s original Tom Sawyer.
Google‟s process of converting the billions of metadata records into a single nonredundant database of
book editions consists of the following principal steps:
3
1. Coarsely dividing the billions of metadata records into groups that may refer to the same
work (e.g., Tom Sawyer).
2. Identifying and aggregating multivolume works based on the presence of cues from individual
records.
3. Subdividing the group of records corresponding to each work into constituent groups
corresponding to the various editions (e.g., the 1909 publication of De lotgevallen van Tom
Sawyer, translated from English to Dutch by Johan Braakensiek).
4. Merging the records for each edition into a new “consensus” record.
The result is a set of consensus records, where each record corresponds to a distinct book edition and
work, and where the contents of each record are formed out of fields from multiple sources. The number
of records in this set -- i.e., the number of known book editions -- increases every year as more books are
written.
In August 2010, this evaluation identified 129 million editions, which is the working estimate we use in this
paper of all the editions ever published (this includes serials and sets but excludes kits, mixed media, and
periodicals such as newspapers). This final database contains bibliographic information for each of these
129 million editions (Ref. S1). The country of publication is known for 85.3% of these editions, authors for
87.8%, publication dates for 92.6%, and the language for 91.6%. Of the 15 million books scanned, the
country of publication is known for 91.5%, authors for 92.1%, publication dates for 95.1%, and the
language for 98.6%.
I.2. Digitization
We describe the way books are scanned and digitized. For publisher-provided books, Google removes
the spines and scans the pages with industrial sheet-fed scanners. For library-provided books, Google
uses custom-built scanning stations designed to impose only as much wear on the book as would result
from someone reading the book. As the pages are turned, stereo cameras overhead photograph each
page, as shown in Figure S1.
One crucial difference between sheet-fed scanners and the stereo scanning process is the flatness of the
page as the image is captured. In sheet-fed scanning, the page is kept flat, similar to conventional flatbed
scanners. With stereo scanning, the book is cradled at an angle that minimizes stress on the spine of the
book (this angle is not shown in Figure S1). Though less damaging to the book, a disadvantage of the
latter approach is that it results in a page that is curved relative to the plane of the camera. The curvature
changes every time a page is turned, for several reasons: the attachment point of the page in the spine
differs, the two stacks of pages change in thickness, and the tension with which the book is held open
may vary. Thicker books have more page curvature and more variation in curvature.
This curvature is measured by projecting a fixed infrared pattern onto each page of the book,
subsequently captured by cameras. When the image is later processed, this pattern is used to identify the
location of the spine and to determine the curvature of the page. Using this curvature information, the
scanned image of each page is digitally resampled so that the results correspond as closely as possible
to the results of sheet-fed scanning. The raw images are also digitally cropped, cleaned, and contrast
enhanced. Blurred pages are automatically detected and rescanned. Details of this approach can be
found in U.S. Patents 7463772 and 7508978; sample results are shown in Figure S2.
Finally, blocks of text are identified and optical character recognition (OCR) is used to convert those
images into digital characters and words, in an approach described elsewhere (Ref. S2). The difficulty of
applying conventional OCR techniques to Google‟s scanning effort is compounded because of variations
in language, font, size, paper quality, and the physical condition of the books being scanned.
Nevertheless, Google estimates that over 98% of words are correctly digitized for modern English books.
After OCR, initial and trailing punctuation is stripped and word fragments split by hyphens are joined,
yielding a stream of words suitable for subsequent indexing.
I.3. Structure Extraction
After the book has been scanned and digitized, the components of the scanned material are classified
into various types. For instance, individual pages are scanned in order to identify which pages comprise
the authored content of the book, as opposed to the pages which comprise frontmatter and backmatter,
such as copyright pages, tables of contents, index pages, etc. Within each page, we also identify
repeated structural elements, such as headers, footers, and page numbers.
Using OCR results from the frontmatter and backmatter, we automatically extract author names, titles,
ISBNs, and other identifying information. This information is used to confirm that the correct consensus
record has been associated with the scanned text.
4
II. Construction of Historical N-grams Corpora
As noted in the paper text, we did not analyze the entire set of 15 million books digitized by Google.
Instead, we
1. Performed further filtering steps to select only a subset of books with highly accurate metadata.
2. Subdivided the books into „base corpora‟ using such metadata fields as language, country of
publication, and subject.
3. For each base corpus, construct a massive numerical table that lists, for each n-gram (often a
word or phrase), how often it appears in the given base corpus in every single year between 1550
and 2008.
In this section, we will describe these three steps. These additional steps ensure high data quality, and
also make it possible to examine historical trends without violating the 'fair use' principle of copyright law:
our object of study is the frequency tables produced in step 3 (which are available as supplemental data),
and not the full-text of the books.
II.1. Additional filtering of books
II.1A. Accuracy of Date-of-Publication metadata
Accurate date-of-publication data is crucial component in the production of time-resolved n-grams data.
Because our study focused most centrally on the English language corpus, we decided to apply more
stringent inclusion criteria in order to make sure the accuracy of the date-of-publication data was as high
as possible.
We found that the lion's share of date-of-publication errors were due to so-called 'bound-withs' - single
volumes that contain multiple works, such as anthologies or collected works of a given author. Among
these bound-withs, the most inaccurately dated subclass were serial publications, such as journals and
periodicals. For instance, many journals had publication dates which were erroneously attributed to the
year in which the first issue of the journal had been published. These journals and serial publications also
represented a different aspect of culture than the books did. For these reasons, we decided to filter out all
serial publications to the extent possible. Our 'Serial Killer' algorithm removed serial publications by
looking for suggestive metadata entries, containing one or more of the following:
5
1. Serial-associated titles, containing such phrases as 'Journal of', 'US Government report', etc.
2. Serial-associated authors, such as those in which the author field is blank, too numerous, or
contains words such as 'committee'.
Note that the match is case-insensitive, and it must be to a complete word in the title; thus the filtering of
titles containing the word „digest‟ does not lead to the removal of works with „digestion‟ in the title. The
entire list of serial-associated title phrases and serial-associated author phrases is included as
supplemental data (Appendix). For English books, 29.4% of books were filtered using the 'Serial Killer',
with the title filter removing 2% and the author filter removing 27.4%. Foreign language corpora were
filtered in a similar fashion.
This filtering step markedly increased the accuracy of the metadata dates. We determined metadata
accuracy by examining 1000 filtered volumes distributed uniformly over time from 1801-2000 (5 per year).
An annotator with no knowledge of our study manually determined the date-of-publication. The annotator
was aware of the Google metadata dates during this process. We found that 5.8% of English books had
metadata dates that were more than 5 years from the date determined by a human examining the book.
Because errors are much more common among older books, and because the actual corpora are strongly
biased toward recent works, the likelihood of error in a randomly sampled book from the final corpus is
much lower than 6.2%. As a point of comparison, 27 of 100 books (27%) selected at random from an
unfiltered corpus contained date-of-publication errors of greater than 5 years. The unfiltered corpus was
created using a sampling strategy similar to that of Eng-1M. This selection mechanism favored recent
books (which are more frequent) and pre-1800 books, which were excluded in the sampling strategy for
filtered books; as such the two numbers (6.2% and 27%) give a sense of the improvement, but are not
strictly comparable.
Note that since the base corpora were generated (August 2009), many additional improvements have
been made to the metadata dates used in Google Book Search itself. As such, these numbers do not
reflect the accuracy of the Google Book Search online tool.
II.1B. OCR quality
The challenge of performing accurate OCR on the entire books dataset is compounded by variations in
such factors as language, font, size, legibility, and physical condition of the book. OCR quality was
assessed using an algorithm developed by Popat et al. (Ref S3). This algorithm yields a probability that
expresses the confidence that a given sequence of text generated by OCR is correct. Incorrect or
anomalous text can result from gross imperfections in the scanned images, or as a result of markings or
drawings. This algorithm uses sophisticated statistics, a variant of the Partial by Partial Matching (PPM)
model, to compute for each glyph (character) the probability that it is anomalous given other nearby
glyphs. ('Nearby' refers to 2-dimensional distance on the original scanned image, hence glyphs above,
below, to the left, and to the right of the target glyph.) The model parameters are tuned using multilanguage
subcorpora, one in each of the 32 supported languages. From the per-glyph probability one can
compute an aggregate probability for a sequence of glyphs, including the entire text of a volume. In this
manner, every volume has associated with it a probabilistic OCR quality score (quantized to an integer
between 0-100; note that the OCR quality score should not be confused with character or word accuracy).
In addition to error detection, the Popat model is also capable of computing the probability that the text is
in a particular language given any sequence of characters. Thus the algorithm serves the dual purpose of
detecting anomalous text while simultaneously identifying the language in which the text is written.
To ensure the highest quality data, we excluded volumes with poor OCR quality. For the languages that
use a Latin alphabet (English, French, Spanish, and German), the OCR quality is generally higher, and
more books are available. As a result, we filtered out all volumes whose quality score was lower than
80%. For Chinese and Russian, fewer books were available, and we did not apply the OCR filter. For
Hebrew, a 50% threshold was used, because its OCR quality was relatively better than Chinese or
Russian. For geographically specific corpora, English US and English UK, a less stringent 60% threshold
was used, in order to maximize the number of books included (note that, as such, these two corpora are
not strict subsets of the broader English corpus). Figure S4 shows the distribution of OCR quality score
as a function of the fraction of books in the English corpus. Use of an 80% cut off will remove the books
with the worst OCR, while retaining the vast majority of the books in the original corpus.
The OCR quality scores were also used as a localized indicator of textual quality in order to remove
anomalous sections of otherwise high-quality texts. The end source text was ensured to be of
comparable quality to the post-OCR text presented in "text-mode" on the Google Books website.
II.1C. Accuracy of language metadata
We applied additional filters to remove books with dubious language-of-composition metadata. This filter
removed volumes whose meta-data language tag disagrees with the language determined by the
statistical language detection algorithm described in section 2A. For our English corpus, 8.56%
6
(approximately 235,000) of the books were filtered out in this way. Table S1 lists the fraction removed at
this stage for our other non-English corpora.
II.1D. Year Restriction
In order to further ensure publication date accuracy and consistency of dates across all our corpora, we
implemented a publication year restriction and only retained books with publication years starting from
1550 and ending in 2008. We found that a significant fraction of mis-dated books have a publication year
of 0 or dates prior to the invention of printing. The number of books filtered due to this year range
restriction is considerably small, usually under 2% of the original number of books.
The fraction of the corpus removed by all stages of the filtering is summarized in Table S1. Note that
because the filters are applied in a fixed order, the statistics presented below are influenced by the
sequence in which the filters were applied. For example, books that trigger both the OCR quality filter and
by the language correction filter are excluded by the OCR quality filter, which is performed first. Of course,
the actual subset of books filtered is the same regardless of the order in which the filters are applied.
II.2. Metadata based subdivision of the Google Books Collection
II.2A. Determination of language
To create accurate corpora in particular languages that minimize cross-language contamination, it is
important to be able to accurately associate books with the language in which they were written. To
determine the language in which a text is written, we rely on metadata derived from our 100 bibliographic
sources, as well as statistical language determination using the Popat algorithm (Ref S3). The algorithm
takes advantage of the fact that certain character sequences, such as 'the', 'of', and 'ion", occur more
frequently in English. In contrast, the sequences 'la', 'aux', and 'de' occur more frequently in French.
These patterns can be used to distinguish between books written in English and those written in
French. More generally, given the entire text of a book, the algorithm can reliably classify the book into
one of the 32 supported language types. The final consensus language was determined based on the
metadata sources as well as the results of the statistical language determination algorithm, with the
statistical algorithm as the higher priority.
II.2B. Determination of book subject assignments
Book subject assignments were determined using a book's Book Industry Standards and Communication
(BISAC) subject categories. BISAC subject headings are a system for categorizing books based on
content developed by the BISAC subject codes committee overseen by the Book Industry Study Group.
They are often used for a variety of purposes, such as to determine how books are shelved in stores. For
English, 92.4% of the books had at least one BISAC subject assignment. In cases where there were
multiple subject assignments, we took the more commonly used subject heading and discarded the rest.
II.2C. Determination of book country-of-publication
Country of publication was determined on the basis of our 100 bibliographic sources; 97% of the books
had a country-of-publication assignment. The country code used is the 2 letter code as defined in the ISO
3166-1 alpha-2 standard. More specifically, when constructing our US versus British English corpora, we
used the codes "us" (United States) and "gb" (Great Britain) to filter our volumes.
7
II.3. Construction of historical n-grams corpora
II.3A. Creation of a digital sequence of 1-grams and extraction of n-gram
counts
All input source texts were first converted into UTF-8 encoding before tokenization. Next, the text of each
book was tokenized into a sequence of 1-grams using Google‟s internal tokenization libraries (more
details on this approach can be found in Ref. S4). Tokenization is affected by two processes: (i) the
reliability of the underlying OCR, especially vis-à-vis the position of blank spaces; (ii) the specific
tokenizer rules used to convert the post-OCR text into a sequence of 1-grams.
Ordinarily, the tokenizer separates the character stream into words at the white space characters (\n
[newline]; \t [tab]; \r [carriage return]; “ “ [space]). There are, however, several exceptional cases:
(1) Column-formatting in books often forces the hyphenation of words across lines. Thus the word
“digitized”, may appear on two lines in a book as "digi-<newline>ized". Prior to tokenization, we look for 1-
grams that end with a hyphen ('-') followed by a newline whitespace character. We then concatenate the
hyphen-ending 1-gram to the next 1-gram. In this manner, digi-<newline>tized became “digitized”. This
step takes place prior to any other steps in the tokenization process.
(2) Each of the following characters are always treated as separate words:
! (exclamation-mark)
@ (at)
% (percent)
^ (caret)
* (star)
( (open-round-bracket)
) (close-round-bracket)
[ (open-square-bracket)
] (close-square-bracket)
- (hyphen)
= (equals)
{ (open-curly-bracket)
} (close-curly-bracket)
| (pipe)
\ (backslash)
: (colon)
: (semi-colon)
< (less-than)
8
, (comma)
> (greater-than)
? (question-mark)
/ (forward-slash)
~ (tilde)
` (back-tick)
“ (double quote)
(3) The following characters are not tokenized as separate words:
& (ampersand)
_ (underscore)
Examples of the resulting words include AT&T, R&D, and variable names such as
HKEY_LOCAL_MACHINE.
(4) . (period) is treated as a separate word, except when it is part of a number or price, such as 99.99 or
$999.95. A specific pattern matcher looks for numbers or prices and tokenizes these special strings as
separate words.
(5) $ (dollar-sign) is treated as a separate word, except where it is the first character of a word consisting
entirely of numbers, possibly containing a decimal point. Examples include $71 and $9.95
(6) # (hash) is treated as a separate word, except when it is preceded by a-g, j or x. This covers musical
notes such as A# (A-sharp), and programming languages j#, and x#.
(7) + (plus) is treated as a separate word, except it appears at the end of a sequence of alphanumeric
characters or “+” s. Thus the strings C++ and Na2+ would be treated as single words. These cases
include many programming language names and chemical compound names.
(8) ' (apostrophe/single-quote) is treated as a separate word, except when it precedes the letter s, as in
ALICE'S and Bob's
The tokenization process for Chinese was different. For Chinese, an internal CJK
(Chinese/Japanese/Korean) segmenter was used to break characters into word units. The CJK
segmenter inserts spaces along common semantic boundaries. Hence, 1-grams that appear in the
Chinese simplified corpora will sometimes contain strings with 1 or more Chinese characters.
Given a sequence of n 1-grams, we denote the corresponding n-gram by concatenating the 1-grams with
a plain space character in between. A few examples of the tokenization and 1-gram construction method
are provided in Table S2.
Each book edition was broken down into a series of 1-grams on a page-by-page basis. For each page of
each book, we counted the number of times each 1-gram appeared. We further counted the number of
times each n-gram appeared (e.g., a sequence of n 1-grams) for all n less than or equal to 5. Because
this was done on a page-by-page basis, n-grams that span two consecutive pages were not counted.
9
II.3B. Generation of historical n-grams corpora
To generate a particular historical n-grams corpus, a subset of book editions is chosen to serve as the
base corpus. The chosen editions are divided by publication year. For each publication year, total counts
for each n-gram are obtained by summing n-gram counts for each book edition that was published in that
year. In particular, three counts are generated: (1) the total number of times the n-gram appears; (2) the
number of pages on which the n-gram appears; and (3) the number of books in which the n-gram
appears.
We then generate tables showing all three counts for each n-gram, resolved by year. In order to ensure
that n-grams could not be easily used to identify individual text sources, we did not report counts for any
n-grams that appeared fewer than 40 times in the corpus. (As a point of reference, the total number of 1-
grams that appear in the 3.2 million books written in English with highest date accuracy („eng-all‟, see
below) is 360 billion: a 1-gram that would appear fewer than 40 times occurs at a frequency of the order
of 10 -11 .) As a result, rare spelling and OCR errors were also omitted. Since most n-grams are infrequent,
this also served to dramatically reduce the size of the n-gram tables. Of course, the most robust historical
trends are associated with frequent n-grams, so our ability to discern these trends was not compromised
by this approach.
By dividing the reported counts by the corpus size (measured in either words, pages, or books), it is
possible to determine the normalized frequency with which an n-gram appears in the base corpus. Note
that the different counts can be used for different purposes. The usage frequency of an n-gram,
normalized by the total number of words, reflects both the number of authors using an n-gram, and how
frequently they use it. It can be driven upward markedly by a single author who uses an n-gram very
frequently, for instance in a biography of 'Gottlieb Daimler' which mentions his name many times. This
latter effect is sometimes undesirable. In such cases, it may be preferable to examine the fraction of
books containing a particular n-gram: texts in different books, which are usually written by different
authors, tend to be more independent.
Eleven corpora were generated, based on eleven different subsets of books. Five of these are English
language corpora, and six are foreign language corpora.
Eng-all
This is derived from a base corpus containing all English language books which pass the filters described
in section 1.
Eng-1M
This is derived from a base corpus containing 1 million English language books which passed the filters
described in section 1. The base corpus is a subset of the Eng-all base corpus.
The sampling was constrained in two ways.
First, the texts were re-sampled so as to exhibit a representative subject distribution. Because digitization
depends on the availability of the physical books (from libraries or publishers), we reasoned that digitized
books may be a biased subset of books as a whole. We therefore re-sampled books so as to ensure that
the diversity of book editions included in the corpus for a given year, as reflected by BISAC subject
codes, reflected the diversity of book editions actually published in that year. We estimated the latter
using our metadata database, which reflects the aggregate of our 100 bibliographic sources and includes
10-fold more book editions than the scanned collection.
Second, the total number of books drawn from any given year was capped at 6174. This has the net
effect of ensuring that the total number of books in the corpus is uniform starting around the year 1883.
This was done to ensure that all books passing the quality filters were included in earlier years. This
10
capping strategy also minimizes bias towards modern books that might otherwise result because the
number of books being published has soared in recent decades.
Eng-Modern-1M
This corpus was generated exactly as Eng-1M above, except that it contains no books from before 1800.
Eng-US
This is derived from a base corpus containing all English language books which pass the filters described
in section 1 but having a quality filtering threshold of 60%, and having 'United States' as its country of
publication, reflected by the 2-letter country code "us",
Eng-UK
This is derived from a base corpus containing all English language books which pass the filters described
in section 1 but having a quality filtering threshold of 60%, and having 'United Kingdom' as its country of
publication, reflected by the 2-letter country code "gb",
Fre-all
This is derived from a base corpus containing all French language books which pass the series of filters
described in section 1.
Ger-all
This is derived from a base corpus containing all German language books which pass
the series of filters described in section 1.
Spa-all
This is derived from a base corpus containing all Spanish language books which pass the series of filters
described in section 1.
Rus-all
This is derived from a base corpus containing all Russian language books which pass the series of filters
described in section 1C-D.
Chi-sim-all
This is derived from a base corpus containing all books written using the simplified Chinese character set
which pass the series of filters described in section 1C-D.
Heb-all
This is derived from a base corpus containing all Hebrew language books which pass the series of filter
described in section 1.
11
The computations required to generate these corpora were performed at Google using the MapReduce
framework for distributed computing (Ref S5). Many computers were used as these computations would
take many years on a single ordinary computer.
Note that the ability to study the frequency of words or phrases in English over time was our primary focus
in this study. As such, we went to significant lengths to ensure the quality of the general English corpora
and their date metadata (i.e., Eng-all, Eng-1M, and Eng-Modern-1M). As a result, the accuracy of placeof-publication
data in English is not as reliable as the accuracy of date metadata. In addition, the foreign
language corpora are affected by issues that were improved and largely eliminated in the English data.
For instance, their date metadata is not as accurate. In the case of Hebrew, the metadata for language is
an oversimplification: a significant fraction of the earliest texts annotated as Hebrew are in fact hybrids
formed from Hebrew and Aramaic, the latter written in Hebrew script.
The size of these base corpora is described in Tables S3-S6.
III. Culturomic Analyses
In this section we describe the computational techniques we use to analyze the historical n-grams
corpora.
III.0. General Remarks
III.0.1 On Corpora.
There is significant variation in the quality of the various corpora during various time periods and their
suitability for culturomic research. All the corpora are adequate for the uses to which they are put in the
paper. In particular, the primary object of study in this paper is the English language from 1800-2000; this
corpus during this period is therefore the most carefully curated of the datasets. However, to encourage
further research, we are releasing all available datasets - far more data than was used in the paper. We
therefore take a moment to describe the factors a culturomic researcher ought to consider before relying
on results of new queries not highlighted in the paper.
1) Volume of data sampled. Where the number of books used to count n-gram frequencies is too small,
the signal to noise ratio declines to the point where reliable trends cannot be discerned. For instance, if
an n-gram's actual frequency is 1 part in n, the number of words required to create a single reliable
timepoint must be some multiple of n. In the English language, for instance, we restrict our study to years
past 1800, where at least 40 million words are found each year. Thus an n-gram whose frequency is 1
part per million can be reliably quantified with single-year resolution. In Chinese, there are fewer than 10
million words per year prior to the year 1956. Thus the Chinese corpus in 1956 is not in general as
suitable for reliable quantification as the English corpus in 1800. (In some cases, reducing the resolution
by binning in larger windows can be used to sample lower frequency n-grams in a corpus that is too smal
for single-year resolution.) In sum: for any corpus and any n-gram in any year, one must consider whether
the size of the corpus is sufficient to enable reliable quantitation of that n-gram in that year.
2) Composition of the corpus. The full dataset contains about 4% of all books ever published, which
limits the extent to which it may be biased relative to the ensemble of all surviving books. Still, marked
shifts in composition from one year to another are a potential source of error. For instance, book sampling
patterns differ for the period before the creation of Google Books (2004) as compared to the period
afterward. Thus, it is difficult to compare results from after 2000 with results from before 2000. As a result,
significant changes in culturomic trends past the year 2000 may reflect corpus composition issues. This
was an important reason for our choice of the period between 1800 and 2000 as the target period.
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3) Quality of OCR. This varies from corpus to corpus as described above. For English, we spent a great
deal of time examining the data by hand as an additional check on its reliability. The other corpora may
not be as reliable.
4) Quality of Metadata. Again, the English language corpus was checked very carefully and
systematically on multiple occasions, as described above and in the following sections. The metadata for
the other corpora may not be equally reliable for all periods. In particular, the Hebrew corpus during the
19th century is composed largely of reprinted works, whose original publication dates farpredate the
metadata date for the publication of the particular edition in question. This must be borne in mind for
researchers intent on working with that corpus.
In addition to these four general issues, we note that earlier portions of the Hebrew corpus contain a large
quantity of Aramaic text written in Hebrew script. As these texts often oscillate back and forth between
Hebrew and Aramaic, they are particularly hard to accurately classify.
All the above issues will likely improve in the years to come. In the meanwhile, users must use extra
caution in interpreting the results of culturomic analyses, especially those based on the various non-
English corpora. Nevertheless, as illustrated in the main text, these corpora already contain a great
treasury of useful material, and we have therefore made them available to the scientific community
without delay. We have no doubt that they will enable many more fascinating discoveries.
III.0.2 On the number of books published
In the text, we report that our corpus contains about 4% of all books ever published. Obtaining this
estimate relies on knowing how many books are in the corpus (5,195,769) and estimating the total
number of books ever published. The latter quantity is extremely difficult to estimate, because the record
of published books is fragmentary and incomplete, and because the definition of book is itself
ambiguous.
One way of estimating the number of books ever published is to calculate the number of editions in the
comprehensive catalog of books which was described in Section I of the supplemental materials. This
produces an estimate of 129 million book editions. However, this estimate must be regarded with great
caution: it is conservative, and the choice of parameters for the clustering algorithm can lead to significant
variation in the results. More details are provided in Ref S1.
Another independent estimate we obtained in the study "How Much Information? (2003)" conducted at
Berkeley (Ref S6). That study also produced a very rough estimate of the number of books ever
published and concluded that it was between 74 million and 175 million.
The results of both estimates are in general agreement. If the actual number is closer to the low end of
the Berkeley range, then our 5 million book corpus encompasses a little more than 5% of all books ever
published; if it is at the high end, then our corpus would constitute a little less than 3%. We report an
approximate value (about 4%) in the text; it is clear that, in the coming years, more precise estimates of
the denominator will become available.
III.1. Generation of timeline plots
III.1A. Single Query
The timeline plots shown in the paper are created by taking the number of appearances of an n-gram in a
given year in the specified corpus and dividing by the total number of words in the corpus in that year.
This yields a raw frequency value. Results are smoothed using a three year window; i.e., the frequency of
13
a particular n-gram in year X as shown in the plots is the mean of the raw frequency value for the n-gram
in the year X, the year X-1, and the year X+1.
Note that for each n-gram in the corpus, we can provide three measures as a function of year of
publication:
1- the number of times it appeared
2- the number of pages where it appeared
3- the number of books where it appeared.
Throughout the paper, we make use only of the first measure; but the two others remain available. They
are generally all in agreement, but can denote distinct cultural effects. These distinctions are not explored
in this paper.
For example, we give in Appendix measures for the frequency of the word 'evolution'. In the first three
columns, we give the number of times it appeared, the normalized number of times it appeared (relative
to #words that year), the normalized number of pages it appeared in, and the normalized number of
books it appeared in, as a function of the date.
III.1B. Multiple Query/Cohort Timelines
Where indicated, timeline plots may reflect the aggregates of multiple query results, such as a cohort of
individuals or inventions. In these cases, the raw data for each query we used to associate each year with
a set of frequencies. The plot was generated by choosing a measure of central tendency to characterize
the set of frequencies (either mean or median) and associating the resulting value with the corresponding
year.
Such methods can be confounded by the vast frequency differences among the various constituent
queries. For instance, the mean will tend to be dominated by the most frequent queries, which might be
several orders of magnitude more frequent than the least frequent queries. If the absolute frequency of
the various query results is not of interest, but only their relative change over time, then individual query
results may be normalized so that they yield a total of 1. This results in a probability mass function for
each query describing the likelihood that a random instance of a query derives from a particular year.
These probability mass functions may then be summed to characterize a set of multiple queries. This
approach eliminates bias due to inter-query differences in frequency, making the change over time in the
cohort easier to track.
III.2. Note on collection of historical and cultural data
In performing the analyses described in this paper, we frequently required additional curated datasets of
various cultural facts, such as dates of rule of various monarchs, lists of notable people and inventions,
and many others. We often used Wikipedia in the process of obtaining these lists. Where Wikipedia is
merely digitizing the content available in another source (for instance, the blacklists of Wolfgang
Hermann), we corrected the data using the original sources. In other cases this was not possible, but we
felt that the use of Wikipedia was justifiable given that (i) the data – including all prior versions - is publicly
available; (ii) it was created by third parties with no knowledge of our intended analyses; and (iii) the
specific statistical analyses performed using the data were robust to errors; i.e., they would be valid as
long as most of the information was accurate, even if some fraction of the underlying information was
wrong. (For instance, the aggregate analysis of treaty dates as compared to the timeline of the
corresponding treaty, shown in the control section, will work as long as most of the treaty names and
dates are accurate, even if some fraction of the records is erroneous.
We also used several datasets from the Encyclopedia Britannica, to confirm that our results were
unchanged when high-quality carefully curated data was used. For the lexicographic analyses, we relied
primarily on existing data from the American Heritage Dictionary.
We avoided doing manual annotation ourselves wherever possible, in an effort to avoid biasing the
results. When manual annotation had to be performed, such as in the classification of samples from our
14
language lexica, we tried whenever possible to have the annotation performed by a third party with no
knowledge of the analyses we were undertaking
III.3. Controls
To confirm the quality of our data in the English language, we sought positive controls in the form of
words that should exhibit very strong peaks around a date of interest. We used three categories of such
words: heads of state („President Truman‟), treaties („Treaty of Versailles‟), and geographical name
change („Byelorussia‟ to „Belarus‟). We used Wikipedia as a primary source of such words, and manually
curated the lists as described below. We computed the timeserie of each n-gram, centered it on the date
of interest (year when the person became president, for instance), and normalized the timeserie by
overall frequency. Then, we took the mean trajectory for each of the three cohorts, and plotted in Figure
S5.
The list of heads of states include all US presidents and British monarchs who gained power in the 19 th or
20 th centuries (we removed ambiguous names, such as „President Roosevelt‟). The list of treaties is taken
from the list of 198 treaties signed in the 19 th or 20 th centuries (S7); but we kept only the 121 names that
referred to only one known treaty, and that have non zero timeseries. The list of country name changes is
taken from Ref S8. The lists are given in APPENDIX.
The correspondence between the expected and observed presence of peaks was excellent. 42 out of 44
heads of state had a frequency increase of over 10-fold in the decade after they took office (expected if
the year of interest was random: 1). Similarly, 85 out of 92 treaties had a frequency increase of over 10-
fold in the decade after they were signed (expected: 2). Last, 23 out of 28 new country names became
more frequent than the country name they replaced within 3 years of the name change; exceptions
include Kampuchea/Cambodia (the name Cambodia was later reinstated), Iran/Persia (Iran is still today
referred to as Persia in many contexts) and Sri Lanka/Ceylon (Ceylon is also a popular tea).
III.4. Lexicon Analysis
III.4A. Estimation of the number of 1-grams defined in leading
dictionaries of the English language.
(a) American Heritage Dictionary of the English Language, 4th Edition (2000)
We are indebted to the editorial staff of AHD4 for providing us the list of the 153,459 headwords that
make up the entries of AHD4. However, many headwords are not single words (“preferential voting” or
“men‟s room”), and others are listed as many times as there are grammatical categories (“to console”, the
verb; “console”, the piece of furniture).
Among those entries, we find 116,156 unique 1-grams (such as “materialism” or “extravagate”).
15
(b) Webster’s Third New International Dictionary (2002)
The editorial staff communicated to us the number of “boldface entries” of the dictionary, which are taken
to be the number of n-grams defined: 476,330.
The editorial staff also communicated the number of multi-word entries 74,000 out of a total number of
entries 275,000. They estimate a lower bound of multi-word entries at 27% of the entries.
Therefore, we estimate an upper bound of unique 1-grams defined by this dictionary as 0.27*476,330,
which is approximately 348,000.
(c) Oxford English Dictionary (Reference in main text)
From the website of the OED we can read that the “number of word forms defined and/or illustrated” is
615,100; and that we find 169,000 “italicized-bold phrases and combinations”.
Therefore, we estimate an upper bound of the number of unique 1-grams defined by this dictionary as
615,100-169,000 which is approximately 446,000.
III.4B. Estimation of Lexicon Size
How frequent does a 1-gram have to be in order to be considered a word? We chose a minimum
frequency threshold for „common‟ 1-grams by attempting to identify the largest frequency decile that
remains lower than the frequency of most dictionary words.
We plotted a histogram showing the frequency of the 1-grams defined in AHD4, as measured in our year
2000 lexicon. We found that 90% of 1-gram headwords had a frequency greater than 10 -9 , but only 70%
were more frequent than 10 -8 . Therefore, the frequency 10 -9 is a reasonable threshold for inclusion in the
lexicon.
To estimate the number of words, we began by generating the list of common 1-grams with a higher
chronological resolution, namely 11 different time points from 1900 until 2000 (1900, 1910, 1920, ... 2000)
as described above. We next excluded all 1-grams with non-alphabetical characters in order to produce a
list of common alphabetical forms for each time point.
For three of the time points (1900, 1950, 2000), we took a random sample of 1000 alphabetical forms
from the resulting set of alphabetical forms. These were classified by a native English speaker with no
knowledge of the analyses being performed. The results of the classification are found in Appendix. We
asked the speaker to classify the candidate words were classified into 8 categories:
M if the word is a misspelling or a typo or seems like gibberish*
N if the word derives primarily from a personal or a company name
P for any other kind of proper nouns
H if the word has lost its original hyphen
F if the word is a foreign word not generally used in English sentences
B if it is a „borrowed‟ foreign word that is often used in English sentences
R for anything that does not fall into the above categories
U unclassifiable for some reason
We computed the fraction of these 1000 words at each time point that were classified as P, N, B, or R,
which we call the „word fraction for year X‟, or WF X . To compute the estimated lexicon size for 1900,
1950, and 2000, we multiplied the word fraction by the number of alphabetical forms in those years.
For the other 8 time points, we did not perform a separate sampling step. Instead, we estimated the word
fraction by linearly interpolating the word fraction of the nearest sampled time points; i.e., the word
fraction in 1920 satisfied WF 1920 =.WF 1900 +.4*(WF 1950 .- WF 1900 ). We then multiplied the word fraction by the
number of alphabetical forms in the corresponding year, as above.
For the year 2000 lexicon, we repeated the sampling and annotation process using a different native
speaker. The results were similar, which confirmed that our findings were independent of the person
doing the annotation.
We note that the trends shown in Fig 2A are similar when proper nouns (N) are excluded from the lexicon
(i.e., the only categories are P, B and R). Figure S7 shows the estimates of the lexicon excluding the
category „N‟ (proper nouns).
* A typo is a one-time typing error by someone who presumably knows the correct spelling (as in
improtant); a misspelling, which generally has the same pronunciation as the correct spelling, arises when
a person is ignorant of the correct spelling (as in abberation).
16
III.4C. Dictionary Coverage
To determine the coverage of the OED and Merriam-Webster‟s Unabridge Dictionary (MW), we
performed the above analysis on randomly generated subsets of the lexicon in eight frequency deciles
(ranging from 10 -9 – 10 -8 to 10 -3 – 10 -2 ). The samples contained 500 candidate words each for all but the
top 3 deciles; the samples corresponding to the top 3 deciles (10 -5 – 10 -4 , 10 -4 – 10 -3 , 10 -3 – 10 -2 )
contained 100 candidate words each.
A native speaker with no knowledge of the experiment being performed determined which words from our
random samples fell into the P, B, or R categories (to enable a fair comparison, we excluded the N
category from our analysis as both OED an MW exclude them). The annotator then attempted to find a
definition for the words in both the online edition of the Merriam-Webster Unabridged Dictionary or in the
online version of the Oxford English Dictionary‟s 2 nd edition. Notably, the performance of the latter was
boosted appreciably by its inclusion of Merriam-Webster‟s Medical Dictionary. Results of this analysis are
shown in Appendix.
To estimate the fraction of dark matter in the English language, we applied the formula:
sum over all deciles of P word *P OED/MW *N 1gram , with:
- N 1gram the number of 1grams in the decile
- P word the proportion of words (R,B or P) in this decile
- P OED/MW the proportion of words of that decile that are covered in OED or MW.
We obtain 52% of dark matter, words not listed in either MW or the OED. With the procedure above, we
estimate the number of words excluding proper nouns at 572,000; this results in 297,000 words unlisted
in even the most comprehensive commercial and historical dictionaries.
III.4D. Analysis New and Obsolete words in the American Heritage
Dictionary
We obtained a list of the 4804 vocabulary items that were added to the AHD4 in 2000 from the
dictionary‟s editorial staff. These 4804 words were not in AHD3 (1992) – although, on rare occasions a
word could have featured in earlier editions of the dictionary (this is the case for “gypseous”, which was
included in AHD1 and AHD2).
Similar to our study of the dictionary‟s lexicon, we restrict ourselves to 1grams. We find 2077 1-grams
newly added to the AHD4. Median frequency (Fig 2D) is computed by obtaining all frequencies of this set
of words and computing its median.
Next, we ask which 1grams appear in AHD4 but are not part of the year 2000 lexicon any more
(frequency lower than one part per billion between 1990 and 2000). We compute the lexical frequency of
the 1-gram headwords in AHD, and find a small number (2,220) that are not part of the lexicon today. We
show the mean frequency of these 2,220 words (Fig 2F).
III.5. The Evolution of Grammar
III.5A. Ensemble of verbs studied
Our list of irregular verbs was derived from the supplemental materials of Ref 18 (main text). The full list
of 281 verbs is given in Appendix.
Our objective is to study the way word frequency affects the trajectories of the irregular compared with
regular past tense. To do so, we must be confident that
- the 1grams used refer to the verbs themselves: “to dive/dove” cannot be used, as “dove” is a
common noun for a bird. Or, in the verb “to bet/bet”, the irregular preterit cannot be distinguished from the
17
present (or, for that matter, from the common noun “a bet”).
- the verb is not a compound, like “overpay” or “unbind”, as the effect of the underlying verb
(“pay”, “bind”) is presumably stronger than that of usage frequency.
We therefore obtain a list of 106 verbs that we use in the study (marked by the denomination „True‟ in the
column “Use in the study?”)
III.5B. Verb frequencies
Next, for each verb, we computed the frequency of the regular past tense (built by suffixation of „-ed‟ at
the end of the verb), and the frequency of the irregular past tense (summing preterit and past participle).
These trajectories are represented in Fig 3A and Fig S8.
We define the regularity of a verb: at any given point in time, the regularity of a verb is the percentage of
past tense usage made using the regular version. Therefore, in a given year, the regularity of a verb is
r=R/(R+I) where R is the number of times the regular past tense was used, and I the number of times the
irregular past tense was used. The regularity is a continuous variable that ranges between 0 and 1
(100%).
We plot in Figure 3B the mean regularity between 1800-1825 in x-axis, and the mean regularity between
1975-2000 in y-axis.
If we assume that a speaker of the English language uses only one of the two variants (regular or
irregular); and that all speakers of English are equally likely to use the verb; then the regularity translates
directly into percentage of the population of speakers using the regular form. While these assumptions
may not hold generally, they provide a convenient way of estimating the prevalence of a certain word in
the population of English speakers (or writers).
III.5C. Rates of regularization
We can compute, for any verb, the slope of regularity as a function of time: this can be interpreted as the
variation in percentage of the population of English speakers using the regular form.
By holding population size constant over the time window used to obtain the slope, we derive the
variation of population using the regular form in absolute terms.
For instance, the regularity of “sneak/snuck” has decreased from 100% to 50% over the past 50 years,
which is 1% per year. We consider the population of US English speakers to be roughly 300 million. As a
result, snuck is sneaking in at a speed of 3 million speakers per year, or about one speaker per minute in
the US.
III.5D. Classification of Verbs
The verbs were classified into different types based on the phonetic pattern they represented using the
classification of Ref 18 (main text). Fig 3C shows the median regularity for the verbs „burn‟, „spoil‟, „dwell‟,
„learn‟, „smell‟, „spill‟ in each year. We compute the UK rate as above, using 60 million for UK population.
III.6. Collective Memory
One hundred timelines were generated, for every year between 1875 and 1975. Amplitude for each plot
was measured by either computing „peak height‟ – i.e., the maximum of all the plotted values, or „areaunder-the
curve‟ – i.e., the sum of all the plotted values. The peak for year X always occurred within a
18
handful of years after the year X itself. The lag between a year and its peak is partly due to the length of
the authorship and publication process. For instance, a book about the events of 1950 may be written
over the period from 1950-1952 and only published in 1953.
For each year, we estimated the slope of the exponential decay shortly past its peak. The exponent was
estimated using the slope of the curve on a logarithmic plot of frequency between the year Y+5 and the
year Y+25. This estimate is robust to the specific values of the interval, as long as the first value (here,
Y+5) is past the peak of Y, and the second value is in the fifty years that follow Y. The Inset in Figure 4A
was generated using 5 and 25. The half-life could thus be derived.
Half-life can also be estimated directly by asking how many years past the peak elapse before frequency
drops below half its peak value. These values are noisier, but exhibit the same trend as in Figure 4A,
Inset (not shown).
Trends similar to those described here may capture more general events, such as those shown in Figure
S9.
III.7. The Pursuit of Fame
We study the fame of individuals appearing in the biographical sections of Encyclopedia Britannica and
Wikipedia. Given the encyclopedic objective of these sources, we argue these represent comprehensive
lists of notable individuals. Thus, from Encyclopedia Britannica and Wikipedia, we produce databases of
all individuals born between 1800-1980, recording their full name and year of birth. We develop a method
to identify the most common, relevant names used to refer to all individuals in our databases. This
method enables us to deal with potentially complicated full names, sometimes including multiple titles and
middle names. On the basis of the amount of biographical information regarding each individual, we
resolve the ambiguity arising when multiple individuals share some part, or all, their name. Finally, using
the time series of the word frequency of people‟s name, we compare the fame of individuals born in the
same year or having the same occupation.
III.7A) Complete procedure
7.A.1 - Extraction of individuals appearing in Wikipedia.
Wikipedia is a large encyclopedic information source, with an important number of articles referring to
people. We identify biographical Wikipedia articles through the DBPedia engine (Ref S9), a relational
database created by extensively parsing Wikipedia. For our purposes, the most relevant component of
DBPedia is the “Categories” relational database.
Wikipedia categories are structural entities which unite articles related to a specific topic. The DBPedia
“Categories” database includes, for all articles within Wikipedia, a complete listing of the categories of
which this article is a member. As an example, the article for Albert Einstein
(http://en.wikipedia.org/wiki/Albert_Einstein) is a member of 73 categories, including “German physicists”,
“American physicists”, “Violonists”, “People from Ulm” and “1879_births”. Likewise, the article for Joseph
Heller (http://en.wikipedia.org/wiki/Joseph_Heller) is a member of 23 categories, including “Russian-
American Jews”, “American novelists”, “Catch-22” and “1923_births”.
We recognize articles referring to non-fictional people by their membership in a “year_births” category.
The category “1879_births” includes Albert Einstein, Wallace Stevens and Leon Trotsky ,likewise
“1923_births” includes Henry Kissinger, Maria Callas and Joseph Heller while “1931_births” includes
Michael Gorbachev, Raul Castro and Rupert Murdoch. If only the approximate birth year of a person is
19
known, their article will be a member of a “decade_births” category such as “1890s_births” and
“1930s_births”. We treat these individuals as if born at the beginning of the decade.
For every parsed article, we append metadata relating to the importance of the article within Wikipedia,
namely the size in words of the article and the number of page views which it obtains. The article word
count is created by directly accessing the article using its URL. The traffic statistics for Wikipedia articles
are obtained from http://stats.grok.se/.
Figure S10a displays the number of records parsed from Wikipedia and retained for the final cohort
analysis. Table S7 displays specific examples from the extraction‟s output, including name, year of birth,
year of death, approximate word count of main article and traffic statistics for March 2010.
1) Create a database of records referring to people born 1800-1980 in Wikipedia.
a. Using the DBPedia framework, find all articles which are members of the categories
„1700_births‟ through „1980_births‟. Only people both in 1800-1980 are used for the
purposes of fame analysis. People born in 1700-1799 are used to identify naming
ambiguities as described in section III.7.A.7 of this Supplementary Material.
b. For all these articles, create a record identified by the article URL, and append the birth
year.
c. For every record, use the URL to navigate to the online Wikipedia page. Within the main
article body text, remove all HTML markup tags and perform a word count. Append this
word count to the record.
d. For every record, use the URL to determine the page‟s traffic statistics for the month of
March 2010. Append the number of views to the record.
III.7.A.2 – Identification of occupation for individuals appearing in Wikipedia.
Two types of structural elements within Wikipedia enable us to identify, for certain individuals, their
occupation. The first, Wikipedia Categories, was previously described and used to recognize articles
about people. Wikipedia Categories also contain information pertaining to occupation. The categories
“Physicists”, “Physicists by Nationality”, “Physicists stubs”, along with their subcategories, pinpoint articles
of relating to the occupation of physicist. The second are Wikipedia Lists, special pages dedicated to
listing Wikipedia articles which fit a precise subject. For physicists, relevant examples are “List of
physicists”, “List of plasma physicists” and “List of theoretical physicists”. Given their redundancy, these
two structural elements, when used in combination provide a strong means of identifying the occupation
of an individual.
Next, we selected the top 50 individuals in each category, and annotated each one manually as a function
of the individual‟s main occupation, as determined by reading the associated Wikipedia article. For
instance, “Che Guevara” was listed in Biologists; so even though he was a medical doctor by training, this
is not his primary historical contribution. The most famous individuals of each category born between
1800 and 1920 are given in Appendix.
In our database of individuals, we append, when available, information about the occupations of people.
This enables the comparison, on the basis of fame, of groups of individuals distinguished by their
occupational decisions.
20
2) Associate Wikipedia records of individuals with occupations using relevant Wikipedia
“Categories” and “Lists” pages. For every occupation to be investigated :
a. Manually create a list of Wikipedia categories and lists associated with this defined
occupation.
b. Using the DBPedia framework, find all the Wikipedia articles which are members of the
chosen Wikipedia categories.
c. Using the online Wikipedia website, find all Wikipedia articles which are listed in the body
of the chosen Wikipedia lists.
d. Intersect the set of all articles belonging to the relevant Lists and Categories with the set
of people both 1800-1980. For people in both sets, append the occupation information.
e. Associate the records of these articles with the occupation.
III.7.A.3 - Extraction of individuals appearing in Encyclopedia Britannica.
Encyclopedia Britannica is a hand-curated, high quality encyclopedic dataset with many detailed
biographical entries. We obtained, in a private communication, structured datasets from Encyclopedia
Britannica Inc. These datasets contain a complete record of all entries relating to individuals in the
Encyclopedia Britannica. Each record contains the birth and death of the person at hand, as well as set of
information snippets summarizing the most critical biographical information available within the
encyclopedia.
For the analysis of fame, we extract, from the dataset provided by Encyclopedia Britannica Inc.,
records of individuals born in between 1800 and 1980. For every person, we retain, as a measure of their
notability, a count of the number of biographical snippets present in the dataset. Figure S10b outlines the
number of records parsed from the Encyclopedia Britannica dataset, as well as the number of these
records ultimately retained for final analysis. Table S8 displays examples of records parsed in this step of
the analysis procedure.
3) Create a database of records referring to people born 1800-1980 in Encyclopedia
Britannica.
a. Using the internal database records provided by Encyclopedia Britannica Inc., find all
entries referring to individuals born 1700-1980. Only people both in 1800-1980 are used
for the purposes of fame analysis. People born in 1700-1799 are used to identify naming
ambiguities as described in section III.7.A.7 of this Supplementary Material.
b. For these entries, create a record identified by a unique integer containing the individual‟s
full name, as listed in the encyclopedia, and the individual‟s birth year.
c. For every record, find the number of encyclopedic informational snippets present in the
Encyclopedia Britannica dataset. Append this count to the record.
III.7.A.4 – Produce spelling variants of the full names of individuals.
We ultimately wish to identify the most relevant name used to commonly refer to an individual. Given the
limits of OCR and the specificities of the method used to create the word frequency database, certain
typographic elements such as accents, hyphens or quotation marks can complicate this process. As
such, for every full name present in our database of people, we append variants of the full names where
these typographic elements have been removed or, when possible, replaced. Table S9 presents
examples of spelling variants for multiple names.
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4) In both databases, for every record, create a set of raw names variants. To create the set:
a. Include the original raw name.
b. If the name includes apostrophes or quotation marks, include a variant where these
elements are removed.
c. If the first word in the name contains a hyphen, include a name where this hyphen is
replaced with a whitespace.
d. If the last word of the name is a numeral, include a name where this numeral has been
removed.
e. For every element in the set which contains non-Latin characters, include a variant where
this characters have been replaced using the closest Latin equivalent.
III.7.A.5 – Find possible names used to refer to individuals.
The common name of an individual sometimes significantly differs from the complete, formal name
present in Encyclopedia Britannica and Wikipedia. This encyclopedia full name can contain details such
as titles, initials and military or nobility standings, which are not commonly used when referring to
individual in most publications. Even in simpler cases, when the full name contains only first, middle and
last names, there exists no systematic convention on which names to use when talking about an
individual. Henry David Thoreau is most commonly referred to by his full name, not “Henry Thoreau” nor
“David Thoreau”, whereas Oliver Joseph Lodge is mentioned by his first and last name “Oliver Lodge”,
not his full name “Oliver Joseph Lodge”.
Given a full name with complex structure potentially containing details such as titles, initials, nobility rights
and ranks, in addition to multiple first and last names, we must extract a list of simple names, using three
words at most, which can potentially be used to refer to this individual. This set of names is created by
generating combinations of names found in the raw name. Furthermore, whenever they appear we
systematically exclude common words such as titles or ranks from these names. The query name sets of
several individuals are displayed in Table S10.
5) For every record, using the set of raw names, create a set of query names. Query names
are (2,3) grams which will be used in order to measure the fame of the individual. The following
procedure is iterated on every raw name variant associated with the record. Steps for which the
record type is not specified are carried out for both.
a. For Encyclopedia Britannica records, truncate the raw name at the second comma,
reorder so that the part of name preceding the first comma follows that succeeding the
comma.
b. For Wikipedia records, replace the underscores with whitespaces.
c. Truncate the name string at the first (if any) parenthesis or comma.
d. Truncate the name string at the beginning of the words „in‟, ‟In‟, ‟the‟, ‟The‟, ‟of‟ and „Of‟, if
these are present.
e. Create the last name set. Iterating from last to first in the words of the name, add the first
name with the following properties:
i. Begin with a capitalized letter.
ii. Longer than 1 character.
iii. Not ending in a period.
iv. If the words preceding this last name are identified as a prefix ('von', 'de', 'van',
'der', 'de' , „d'‟, 'al-', 'la', 'da', 'the', 'le', 'du', 'bin', 'y', 'ibn' and their capitalized
versions ), the last name is a 2gram containing both the prefix.
f. If the last name contains a capitalized character besides the first one, add a variant of
this word where the only capital letter is the first to the set of last names.
g. Create the set of first names. Iterating on the raw name elements which are not part of
the last name set, candidate first names are words with the following properties :
i. Begin with a capital letter.
ii. Longer than 1 character.
iii. Not ending in a period.
iv. Not a title. („Archduke‟, 'Saint', 'Emperor', 'Empress', 'Mademoiselle', 'Mother',
'Brother', 'Sister', 'Father', 'Mr', 'Mrs', 'Marshall', 'Justice', 'Cardinal', 'Archbishop',
'Senator', 'President', 'Colonel', 'General', 'Admiral', 'Sir', 'Lady', 'Prince',
'Princess', 'King', 'Queen', 'de', 'Baron', 'Baroness', 'Grand', 'Duchess', 'Duke',
'Lord', 'Count', 'Countess', 'Dr')
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h. Add to the set of query names all pairs of “first names + last names” produced by
combining the sets of first and last names.
i. This procedure is carried for every raw name variant.
III.7.A.6 – Find the word match frequencies of all names.
Given the set of names which may refer to an individual, we wish to find the time resolved words
frequencies of these names. The frequency of the name, which corresponds to a measure of how often
an individual is mentioned, provides a metric for the fame of that person. We append the word
frequencies of all the names which can potentially refer to an individual. This enables us, in a later step,
to identify which name is the relevant.
6) Append the fame signal for each query name of each record. The fame signal is the
timeseries of normalized word matches in the complete English database.
III.7.A.7 – Find ambiguous names which can refer to multiple individuals.
Certain names are particularly popular and are shared by multiple people. This results in ambiguity, as
the same query name may refer to a plurality of individuals. Homonimity conflicts occur between a group
of individuals when they share some part of, or all, their name. When these homonimity conflicts arise,
the word frequency of a specific name may not reflect the number of references to a unique person, but to
that of an entire group. As such, the word frequency does not constitute a clear means of tracking the
fame of the concerned individuals. We identify homonimity conflicts by finding instances of individuals
whose names contain complete or partial matches. These conflicts are, when possible, resolved on the
basis of the importance of the conflicted individuals in the following step. Typical homonimity conflicts are
shown in Table S11.
7) Identify homonimity conflicts. Homonimity conflicts arise when the query names of two or more
individuals contain a substring match. These conflicts are distinguished as such :
a. For every query name of every record, find the set of substrings of query names.
b. For every query name of every record, search for matches in the set of query name
substrings of all other records.
c. Bidirectional homonimity conflicts occur when a query name fully matches another query
name. The name conflicted name could be used to refer to both individuals.
Unidirectional conflicts occur when a query name has a substring match within another
query name. Thus, the conflicted name can refer to one of the individuals, but also be
part of a name referring to another.
III.7.A.8 – Resolve, when possible, the most likely origin of ambiguous names.
The problem of homonymous individuals is limiting because the word frequencies data do not allow us to
resolve the true identity behind a homonymous name. Nonetheless, in some cases, it is possible to
distinguish conflicted individuals on the basis of their importance. For the database of people extracted
from Encyclopedia Britannica, we argue that the quantity of information available about an individual
provides a proxy for their relevance. Likewise, for people obtained from Wikipedia, we can judge their
importance by the size of the article written about the person and the quantity of traffic the article
generates. As such, we approach the problem of ambiguous names by comparing the notability of
individuals, as evaluated by the amount of information available about them in the respective
encyclopedic source. Examples of conflict resolution are shown in Table S12 and S13.
8) Resolve homonimity conflicts.
23
a. Conflict resolution involves the decision of whether a query name, associated with
multiple records, can unambiguously refer to a single one of them.
b. Wikipedia. Conflict resolution for Wikipedia records is carried out on the basis the main
article word count and traffic statistics. A conflict is resolved as such :
i. Find the cumulative word count of words written in the articles in conflict.
ii. Find the cumulative number of views resulting from the traffic to the articles in
conflict.
iii. For every record in the conflict, find the fraction of words and views resulting from
this record by dividing by the cumulative counts.
iv. Does a record have the largest fraction of both words written and page views?
v. Does this record have above 66% of either words written and page views?
vi. If so, the conflicted query name can be considered as being sufficiently specific
to the record with these properties.
c. Encyclopedia Britannica. Conflict resolution for Encyclopedia Britannica records is carried
on the basis of the quantity of information snippets present in the dataset.
i. Find the cumulative number of information snippets related to the records in
conflicts.
ii. For every record in the conflict, find the fraction of informational snippets by
dividing with the cumulative count
iii. If a record has greater than 66% of the cumulative total, the query name in
conflict is considered to refer to this record.
III.7.A.9 Identify the most relevant name used to refer to an individual.
So far, we have obtained, for all individuals in both our databases, a set of names by which they can
plausibly be mentioned. From this set, we wish to identify the best such candidate and use its word
frequency to observe the fame of the person at hand. This optimal name is identified on the basis of the
amplitude of the word frequency, the potential ambiguities which arise from name homonimity and the
quality of the word frequency time series. Examples are shown in Fig S11 and S12.
9) Determine the best query name for every record.
a. Order all the query names associated with a record on the basis of the integral of the
fame signal from the year of birth until the year 2000.
b. Iterating from the strongest fame signal to the lowest, the selected query name is the first
result with the following properties :
i. Unambiguously refers to the record (as determined by conflict resolution, if
needed).
ii. The average fame signal in the window [year of birth ± 10 years] is less than 10 -9
or an order of magnitude less than the average fame signal from the year of birth
to the year 2000.
iii. (Wikipedia Only). The query name, when converted to a Wikipedia URL by
replacing whitespaces with underscores, refers to the record or an inexistent
article. If the name refers to another article or a disambiguation page, the query
name is rejected.
c. If the best query name is a 2-gram name corresponding the last two names in 3-gram
query name, and if the fame integral of the 3-gram name is 80% of the fame integral of
the 2-gram, the best query name is replaced by the 3-gram.
24
III.7.A.10 – Compare the fame of multiple individuals.
Having identified the best name candidate for every individual, we use the word frequency time series of
this name as a metric for the fame of the each individual. We now compare the fame of multiple
individuals on the basis of the properties of their fame signal. For this analysis, we group people
according to specific characteristics, which in the context of this work are the years of birth and the
respective occupations.
10) Assemble cohorts on the basis of a shared record property.
a. Fetch all records which match a specific record property, such as year of birth or
occupation.
b. Create fame cohorts comparing the fame of individuals born in the same year.
i. Use average lifetime fame ranking, done on the basis of the average fame as
computed from the birth of the individual to the year 2000.
c. Create fame cohorts for individuals with the same occupation.
i. Use most famous 20 th year, ranking on the basis of the 20 th best year in the
terms of fame for the individual.
III.7B. Cohorts of fame
For each year, we defined a cohort of the top 50 most famous individuals born that year. Individual fame
was measured in this case by the average frequency over all years after one's birth. We can compute
cohorts on the basis of names from Wikipedia, or Encyclopedia Britannica. In Figure 5, we used cohorts
computed with names from Wikipedia.
At each time point, we defined the frequency of the cohort as the median value of the frequencies of all
individuals in the cohort.
For each cohort, we define:
(1) Age of initial celebrity. This is the first age when the cohort's frequency is greater than 10-9. This
corresponds to the point at which the median individual in the cohort enter the "English lexicon" as
defined in the first section of the paper.
(2) Age of peak celebrity. This is the first age when the cohort's frequency is greater than 95% of its peak
value. This definition is meant to diminish the noise that exists on the exact position of the peak value of
the cohort's frequency.
(3) Doubling time of fame. We compute the exponential rate at which fame increases between the 'age of
fame' and the 'age of peak fame'. To do so, we fit an exponential to the timeseries with the methods of
least squares. The doubling time is derived from the estimated exponent.
(4) Half-life of fame. We compute the exponential rate at which fame decreases past the year at which it
reaches its peak (which is later than the "age of peak celebrity" as defined above). To do so, we fit an
exponential to the timeseries with the methods of least squares. The half-life is derived from the
estimated exponent.
We show the way these parameters change with the cohort‟s year of birth in Figure S13.
The dynamics of these quantities is sensibly the same when using cohorts from Wikipedia or from
Encyclopedia Britannica. However, Britannica features fewer individuals in their cohorts, and therefore the
cohorts from the early 19 th century are much noisier. We show in Figure S14 the fame analysis
conducted with cohorts from Britannica, restricting our analysis to the years 1840-1950.
In Figure 5E, we analyze the trade-offs between early celebrity and overall fame as a function of
occupation. For each occupation, we select the top 25 most famous individuals born between 1800 and
1920. For each occupation, we define the contour within which all points are close to at least 2 member of
the cohort (it is the contour of the density map created by the cohort).
25
People leave more behind them than a name. Like her fictional protagonist Victor Frankenstein, Mary
Shelley is survived by her creation: Frankenstein took on a life of his own within our collective imagination
(Figure S15). Such legacies, and all the many other ways in which people achieve cultural immortality,
fall beyond the scope of this initial examination.
III.8. History of Technology
A list of inventions from 1800-1960 was taken from Wikipedia (Ref S10).
The year listed is used in our analysis. Where multiple listings of a particular invention appear, the year
retained in the list is the one reported in the main Wikipedia article for the invention. (e.g. "Microwave
Oven" is listed in 1945 and 1946; the main article lists 1945 as the year of invention, and this is the year
we use in our analyses).
Each entry's main Wikipedia page was checked for alternate terms for the invention. Where alternate
names were listed in the main article (e.g. thiamine or thiamin or vitamin B 1 ), all the terms were
compared for their presence in the database. Where there was no single dominant term (e.g.MSG or
monosodium glutamate) the invention was eliminated from the list. If a name other than the originally
listed one appears to be dominant, the dominant name was used in the analysis (e.g.
electroencephalograph and EEG - EEG is used).
Inventions were grouped into 40-year intervals (1800-1840, 1840-1880, 1880-1920, and 1920-1960), and
the median percentages of peak frequency was calculated for each bin for each year following invention:
these were plotted in Fig 4B, together with examples of individual inventions in inset.
Our study of the history of technology suffers from a possible sampling bias: it is possible that some older
inventions, which peaked shortly after their invention, are by now forgotten and not listed in the Wikipedia
article at all. This sampling bias would be more extreme for the earlier cohorts, and would therefore tend
to exaggerate the lag between invention date and cultural impact in the older invention cohorts. We have
verified that our inventions are past their peaks, in all three cohorts (Fig S16). Future analyses would
benefit from the use of historical invention lists to control for this effect.
Another possible bias is that observing inventions later after they were invented leaves more room for the
fame of these inventions to rise. To ensure that the effect we observe is not biased in this way, we
reproduce the analysis done in the paper using constant time intervals: a hundred years from time of
invention. Because we have a narrower timespan, we consider only technologies invented in the 19 th
century; and we group them in only two cohorts. The effect is consistent with that observed in the main
text (Fig S16).
III.9. Censorship
III.9A. Comparing the influence of censorship and propaganda on
various groups
To create panel E of Fig 6, we analyzed a series of cohorts; for each cohort, we display the mean of the
normalized probability mass functions of the cohort, as described in section 1B. We multiplied the result
by 100 in order to represent the probability mass functions more intuitively, as a percentage of lifetime
26
fame. People whose names did not appear in the cohorts for the time periods in question (1925-1933,
1933-1945, and 1955-1965) were eliminated from the analysis.
The cohorts we generated were based on four major sources, and their content is given in Appendix.
1) The Hermann lists
The lists of the infamous librarian Wolfgang Hermann were originally published in a librarianship journal
and later in Boersenblatt, a publishing industry magazine in Germany. They are reproduced in Ref S11. A
digital version is available on the German-language version of Wikipedia (Ref S12). We considered
digitizing Ref S10 by hand to ensure accuracy, but felt that both OCR and manual entry would be timeconsuming
and error prone. Consequently, we began with the list available on Wikipedia and hired a
manual annotator to compare this list with the version appearing in Ref S11 to ensure the accuracy of the
resulting list. The annotator did not have access to our data and made these decisions purely on the
basis of the text of Ref S11. The following changes were made:
Literature
1) “Fjodor Panfjorow” was changed to “Fjodor Panferov”.
2) “Nelly Sachs” was deleted.
History
1) “Hegemann W. Ellwald, Fr. v.” was changed to “W. Hegemann” and “Fr. Von Hellwald”
Art
4) “Paul Stefan” was deleted.
Philosophy/Religion
1) “Max Nitsche” was deleted.
The results of this manual correction process were used as our lists for Politics, Literature, Literary
History, History, Art-related Writers, and Philosophy/Religion.
27
2) The Berlin list
The lists of Hermann continued to be expanded by the Nazi regime. We also analyzed a version from
1938 (Ref S13). This version was digitized by the City of Berlin to mark the 75 th year after the book
burnings in 2008 (Ref S14). The list of authors appearing on the website occasionally included multiple
authors on a single line, or errors in which the author field did not actually contain the name of a person
who wrote the text. These were corrected by hand to create an initial list.
We noted that many authors were listed only using a last name and a first initial. Our manual annotator
attempted to determine the full name of any such author. The results were far from comprehensive, but
did lead us to expand the dataset somewhat; names with only first initials were replaced by the full name
wherever possible.
Some authors were listed using a pseudonym, and on several occasions our manual annotator was able
to determine the real name of the author who used a given pseudonym. In this case, the real name was
added to the list.
In addition, we occasionally included multiple spelling variants for a single author. Because of this, and
because an author‟s real name and pseudonym may both be included on the list, the number of author
names on the list very slightly exceeds the number of individuals being examined. The numbers reported
in the figure are the number of names on the list.
It is worth pointing out that Adolf Hitler appears as an author of one of the banned books from 1938. This
is due to a French version of Mein Kampf, together with commentary, which was banned by the Nazi
authorities. Although it is extremely peculiar to find Hitler on a list of banned authors, we did not remove
Hitler‟s name, as we had no basis for doing so from the standpoint of the technical authorship and name
criteria described above: Adolf Hitler is indeed listed as the author of a book that was banned by the Nazi
regime. This is consistent with our stance throughout the paper, which is that we avoided making
judgments ourselves that could bias the outcome of our results. Instead, we relied strictly upon our
secondary sources. Because Adolf Hitler is only one of many names, the list as a whole nevertheless
exhibits strong evidence of suppression, especially because the measure we retained (median usage) is
robust to such outliers.
3) Degenerate artists
The list of degenerate artists was taken directly from the catalog of a recent exhibition at the Los Angeles
County Museum of Art which endeavored to reconstruct the original „Degenerate Art‟ exhibition (Ref S15).
4) People with recorded ties to Nazis
The list of Nazi party members was generated in a manner consistent with the occupation categories in
section 7. We included the following Wikipedia categories: Nazis_from_outside_Germany, Nazi_leaders,
SS_officers, Holocaust_perpetrators, Officials_of_Nazi_Germany, Nazis_convicted_of_war_crimes,
together with all of their subcategories, with the exception of Nazis_from_outside_Germany. In addition,
the three categories German_Nazi_politicians, Nazi_physicians, Nazis were included without their
respective subcategories.
III.9B. De Novo Identification of Censored and Suppressed Individuals
We began with the list of 56,500 people, comprising the 500 most famous individuals born in each year
from 1800 – 1913. This list was derived from the analysis of all biographies in Wikipedia described in
section 7. We removed all individuals whose mean frequency in the German language corpus was less
than 5 x 10 -9 during the period from 1925 – 1933; because their frequency is low, a statistical assessment
of the effect of censorship and suppression on these individuals is more susceptible to noise.
The suppression index is computed for the remaining individuals using an observed/expected measure.
The expected fame for a given year is computed by taking the mean frequency of the individual in the
German language from 1925-1933, and the mean frequency of the individual from 1955-1965. These two
values are assigned to 1929 and 1960, respectively; linear interpolation is then performed in order to
compute an expected fame value in 1939. This expected value is compared to the observed mean
frequency in the German language during the period from 1933-1945. The ratio of these two numbers is
the suppression index s. The complete list of names and suppression indices is included as supplemental
data. The distribution of s was plotted for using a logarithmic binning strategy, with 100 bins between 10 -2
and 10 2 . Three specific individuals who received scores indicating suppression in German are indicated
on the plot by arrows (Walter Gropius, Pablo Picasso, and Hermann Maas).
As a point of comparison, the entire analysis was repeated for English; these results are shown on the
plot.
III.9C. Validation by an expert annotator
We wanted to see whether the findings of this high-throughput, quantitative approach were consistent
with the conclusions of an expert annotator using traditional, qualitative methods. We created a list of 100
individuals at the extremes of our distribution, including the names of the fifty people with the largest s
value and of the fifty people with the smallest s value. We hired a guide at Yad Vashem with advanced
degrees in German and Jewish literature to manually annotate these 100 names based on her
assessment of which people were suppressed by the Nazis (S), which people would have benefited from
the Nazi regime (B), and lastly, which people would not obviously be affected in either direction (N). All
100 names were presented to the annotator in a single, alphabetized list; the annotator did not have
access to any of our methods, data, or conclusions. Thus the annotator‟s assessment is wholly
independent of our own.
28
The annotator assigned 36 names to the S category and 27 names to the B category; the remaining 37
were given the ambiguous N classification. Of the names assigned to the S category by the human
annotator, 29 had been annotated as suppressed by our algorithm, and 7 as elevated, so the
correspondence between the annotator and our algorithm was 81%. Of the names assigned to the B
category, 25 were annotated as elevated by our algorithm, and only 2 as suppressed, so the
correspondence was 93%.
Taken together, the conclusions of a scholarly annotator researching one name at a time closely matched
those of our automated approach. These findings confirm that our computational method provides an
effective strategy for rapidly identifying likely victims of censorship given a large pool of possibilities.
III.10. Epidemics
Disease epidemics have a significant impact on the surrounding culture (Fig. S18 A-C). It was recently
shown that during seasonal influenza epidemics, users of Google are more likely to engage in influenzarelated
searches, and that this signature of influenza epidemics corresponds well with the results of CDC
surveillance (Ref S16). We therefore reasoned that culturomic approaches might be used to track
historical epidemics. These could help complement historical medical records, which are often woefully
incomplete.
We examined timelines for 4 diseases: influenza (main text), cholera, HIV, and poliomyelitis. In the case
of influenza, peaks in cultural interest showed excellent correspondence with known historical epidemics
(the Russian Flu of 1890, leading to 1M deaths, the Spanish Flu of 1918, leading to 20-100M deaths; and
the Asian Flu of 1957, leading to 1.5M deaths). Similar results were observed for cholera and HIV.
However, results for polio were mixed. The US epidemic of 1916 is clearly observed, but the 1951-55
epidemic is harder to pinpoint: the observed peak is much broader, starting in the 30s and ending in the
60s. This is likely due to increased interest in polio following the election of Franklin Delano Roosevelt in
1932, as well as the development and deployment of Salk‟s polio vaccine in 1952 and Sabin‟s oral
version in 1962. These confounding factors highlight the challenge of interpreting timelines of cultural
interest: interest may increase in response to an epidemic, but it may also respond to a stricken celebrity
or a famous cure.
The dates of important historical epidemics were derived from the Cambridge World History of Human
Diseases (1993) 3 rd Edition.
For cholera, we retained the time periods which most affected the Western world, according to this
resource:
- 1830-35 (Second Cholera Epidemic)
- 1848-52, and 1854 (Third Cholera Epidemic)
- 1866-74 (Fourth Cholera Epidemic)
- 1883-1887 (Fifth Cholera Epidemic)
The first, sixth and seventh cholera epidemics appear not to have caused significant casualties in the
Western world.
29
Supplementary References
“Quantitative analysis of culture using millions of digitized books”,
Michel et al.
S1. L. Taycher, “Books of the world stand up and be counted”,
2010. http://booksearch.blogspot.com/2010/08/books-of-world-stand-up-and-becounted.html
S2. Ray Smith, Daria Antonova, and Dar-Shyang Lee, Adapting the Tesseract
open source OCR engine for multilingual OCR, Proceedings of the
International Conference on Multilingual OCR, Barcelona Spain, 2009,
http://doi.acm.org/10.1145/1577802.1577804
S3. Popat, Ashok. "A panlingual anomalous text detector." DocEng '09: Proceedings
of the 9th ACM symposium on Document Engineering, 2009, pp. 201-204.
S4. Brants, Thorsten and Franz, Alex. "Web 1T 5-gram Version 1." LDC2006T13
http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2006T13
S5. Dean, Jeffrey and Ghemawat, Sanjay. "MapReduce: Simplified Data Processing
on Large Clusters." OSDI '04 p137--150
S6. Lyman, Peter and Hal R. Varian, "How Much Information", 2003.
http://www2.sims.berkeley.edu/research/projects/how-much-info-
2003/print.htm#books
S7. http://en.wikipedia.org/wiki/List_of_treaties.
S8. http://en.wikipedia.org/wiki/Geographical_renaming]
S9. Christian Bizer, Jens Lehmann, Georgi Kobilarov, Sören Auer, Christian Becker,
Richard Cyganiak, Sebastian Hellmann.” DBpedia – A Crystallization Point for
the Web of Data.” Journal of Web Semantics: Science, Services and Agents on
the World Wide Web, 2009, pp. 154–165.
S10. http://en.wikipedia.org/wiki/Timeline_of_historic_inventions
S11. Gerhard Sauder: Die Bücherverbrennung.10. Mai 1933. Ullstein Verlag, Berlin,
Wien 1985.
S12. http://de.wikipedia.org/wiki/Liste_der_verbrannten_Bücher_1933.
S13. Liste Des Schädlichen Und Unerwünschten Schrifttums: Stand Vom 31. Dez.
1938. Leipzig: Hedrich, 1938. Print.
S14. http://www.berlin.de/rubrik/hauptstadt/verbannte_buecher/az-autor.php
S15. Barron, Stephanie, and Peter W. Guenther. Degenerate Art: the Fate of the
Avant-garde in Nazi Germany. Los Angeles, CA: Los Angeles County Museum
of Art, 1991. Print.
S16. Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer,
Mark S. Smolinski, and Larry Brilliant. "Detecting Influenza Epidemics Using
Search Engine Query Data." Nature 457 (2008): 1012-014.
Supplementary Figures
“Quantitative analysis of culture using millions of digitized books”,
Michel et al.
Figure S1
Fig. S1. Schematic of stereo scanning for Google Books.
Figure S2
Fig. S2. Example of a page scanned before (left) and after processing (right).
Figure S3
Fig. S3. Outline of n-gram corpus construction. The numbering corresponds to sections of the text.
Figure S4
Fig. S4. Fraction of English Books with a given OCR quality.
Figure S5
Fig. S5. Known events exhibit sharp peaks at date of occurrence. We select groups of events that occur
at known dates, and produce the corresponding timeseries. We normalize each timeserie relative to its
total frequency, center the timeseries around the relevant event, and plot the mean. (A) A list of 124
treaties. (B) A list of 43 head of state (US presidents, UK monarchs), centered around the year when they
were elected president or became king/queen. (C) A list of 28 country name changes, centered around
the year of name change. Together, these form positive controls about timeseries in the corpus.
Figure S6
Fig. S6. Frequency distribution of words in the dictionary. We compute the frequency in our year 2000
lexicon for all 116,156 words (1-grams) in the AHD (year 2000). We represent the percentage of these
words whose frequency is smaller than the value on the x-axis (logarithmic scale, base 10). 90% of all
words in AHD are more frequent than 1 part per billion (10 -9 ), but only 75% are more frequent than 1
part per 100 million (10 -8 ).
Figure S7
Fig. S7. Lexical trends excluding proper nouns. We compute the number of words that are 1-grams in
the categories “P”, “B” and “R”. The same upward trend starting in 1950 is observed. The size of the
lexicon in the year 2000 is still larger than the OED or W3.
Figure S8
Fig. S8. Example of grammatical change. Irregular verbs are used as a model of grammatical evolution.
For each verb, we plot the usage frequency of its irregular form in red (for instance, ‘found’), and the
usage frequency of its regular past-tense form in blue (for instance, ‘finded’). Virtually all irregular verbs
are found from time to time used in a regular form, but those used more often tend to be used in a
regular way more rarely. This is illustrated in the top two rows with the frequently-used verb “find” and
the less often encountered “dwell”. In the third row, the trajectory of “thrive” is one of many ways by
which regularization occurs. The bottom two panels shows that the regularization of “spill” happened
earlier in the US than in the UK.
Figure S9
Fig. S9. We forget. Events of importance provoke a peak of discussion shortly after they happened, but
interest in them quickly decreases.
Figure S10
Fig. S10. Biographical Records. The number of records parsed from the two encyclopedic sources (blue
curve), and used in our analyses (green curve). See steps 7.A.1 to 7.A.10 above.
Figure S11
Fig. S11. Selection of query name. The chosen query name is in black. (A) Adrien Albert Marie de Mun.
Strongest and optimal query name is Albert de Mun, (B) Oliver Joseph Lodge, strongest and optimal
query name is Oliver Lodge, (C) Henry David Thoreau. Strongest query name is David Thoreau, but is a
substring match of Henry David Thoreau, with fame >80% of David Thoreau. Optimal query name is
Henry David Thoreau. (D) Mary Tyler Moore. Strongest name is Mary Moore, but is rejected because of
noise. Next strongest is Tyler Moor, but this is a substring match of Mary Tyler Moore, with fame >80%
of Tyler Moore. Optimal query name is thus Mary Tyler Moore.
Figure S12
Fig. S12. Filtering out names with trajectories that cannot be resolved. Illustrates the requirement for
query name filtration on the basis of premature fame. Fame a birth is the average fame in a 10 year
window around birth, lifetime fame is the average fame from year of birth to 2000. The dashed line in
(A), (D) indicates the separatrix used to excluded query names with premature fame signals. Points to
the right were rejected from further analysis. In (B), (C), (E), (F) the black line indicates the year of birth
of the individuals whose fame trajectories are plotted.
Figure S13
Fig. S13. Values of the four parameters of fame as a function of time. ‘Age of peak celebrity’ (75 years
old) has been fairly consistent. Celebrities are noticed earlier, and become more famous than ever
before: ‘Age of initial celebrity’ has dropped from 43 to 29 years, and ‘Doubling time’ has dropped from
8.1 to 3.3 years. But they are forgotten sooner as well: the half-life has declined from 120 years to 71.
Figure S14
Fig. S14. Fundamental parameters of fame do not depend on the underlying source of people studied.
We represent the analysis of fame using individuals from Encyclopedia Britannica.
Figure S15
Fig. S15. Many routes to Immortality. People leave more behind them than their name: ‘Mary Shelley’
(blue) created the monstrously famous ‘Frankenstein’ (green).
Figure S16
Fig. S16. Controls. (A) We observe over the same timespan (100 years) two cohorts invented at different
times. Again, the more recent cohort reaches 25% of its peak faster. (B) We verify that inventions have
already reached their peak. We calculate the peak of each invention, and plot the distribution of these
peaks as a function of year, grouping them along the same cohorts as used in the text. In each case, the
distribution falls within the bounds of the period observed (1800-2000).
Figure S17
Fig. S17. Suppression of authors on the Art and Literary History blacklists in German. We plot the
median trajectory (as in the main text) of authors in the Herman lists for Art (green) and Literary History
(red), and for authors found in the 1938 blacklist (blue). The Nazi regime (1933-1945) is highlighted, and
corresponds to strong drops in the trajectories of these authors.
Figure S18
Fig. S18. Tracking historical epidemics using their influence on the surrounding culture. (A) Usage
frequency of various diseases: ‘fever’ (blue), ‘cancer’ (green), ‘asthma’ (red), ‘tuberculosis’ (cyan),
‘diabetes’ (purple), ‘obesity’ (yellow) and ‘heart attack’ (black). (B) Cultural prevalence of AIDS and HIV.
We highlight the year 1983 when the viral agent was discovered. (C) Usage of the term ‘cholera’ peaks
during the cholera epidemics that affected Europe and the US (blue shading). (D) Usage of the term
‘infantile paralysis’ (blue) exhibits one peak during the 1916 polio epidemic (blue shading), and a second
around the time of a series of polio epidemics that took place during the early 1950s. But the second
peak is anomalously broad. Discussion of polio during that time may have been fueled by the election of
‘Franklin Delano Roosevelt’ (green), who had been paralyzed by polio in 1936 (green shading), as well as
by the development of the ‘polio vaccine’ (red) in 1952. The vaccine ultimately eradicated ‘infantile
paralysis’ in the United States.
Figure S19
Fig. S19. Culturomic ‘timelines’ reveal how often a word or phrase appears in books over time. (A) ‘civil
rights’, ‘women’s rights’, ‘children’s rights’ and ‘animals rights’ are shown. (B) ‘genocide’ (blue), ‘the
Holocaust’ (green), and ‘ethnic cleansing’ (red) (C) Ideology: ideas about ‘capitalism’ (blue) and
‘communism’ (green) became extremely important during the 20 th century. The latter peaked during the
1950s and 1960s, but is now decreasing. Sadly, ‘terrorism’ (red) has been on the rise. (D) Climate
change: Awareness of ‘global temperature’, ‘atmospheric CO2’, and ‘sea levels’ is increasing. (E) ‘aspirin’
(blue), ‘penicillin’ (green), ‘antibiotics’ (red), and ‘quinine’ (cyan). (F) ‘germs’ (blue), ‘hygiene’ (green)
and ‘sterilization’ (red). (G) The history of economics: ‘banking’ (blue) is an old concept which was of
central concern during ‘the depression’ (red). Afterwards, a new economic vocabulary arose to
supplement the older ideas. New concepts such as ‘recession’ (cyan), ‘GDP’ (purple), and ‘the economy’
(green) entered everyday discourse. (H) We illustrate geographical name changes: ‘Upper Volta’ (blue)
and ‘Burkina Faso’ (green). (I) ‘radio’ in the US (blue) and in the UK (red) have distinct trajectories. (J)
‘football’ (blue), ‘golf’ (green), ‘baseball’ (red), ‘basketball’ (cyan) and ‘hockey’ (purple) (K) Sportsmen: In
the 1980s, the fame of ‘Michael Jordan’ (cyan) leaped over other that of other great athletes, including
‘Jesse Owens’ (green), ‘Joe Namath’ (red), ‘Mike Tyson’ (purple), and ‘Wayne Gretsky’ (yellow).
Presently, only ‘Babe Ruth’ (blue) can compete. One can only speculate as to whether Jordan’s hang
time will match that of the Bambino. (L) ‘humorless’ is a word that rose to popularity during the first half
of the century. This indicates how these data can serve to identify words that are a marker of a specific
period in time.