DHQ: Digital Humanities Quarterly
2023
Volume 17 Number 2
Volume 17 Number 2
Computational Paremiology: Charting the temporal, ecological dynamics of proverb use in books, news articles, and tweets
Ethan Davis
<edavis_at_lclark_dot_edu>, Computational Story Lab, Vermont Complex Systems Center,
MassMutual Center of Excellence for Complex Systems and Data Science, Vermont
Advanced Computing Core, Watzek Library, Lewis & Clark
College 
Abstract
Proverbs are an essential component of language and culture, and though much attention has been paid to their history and currency, there has been comparatively little quantitative work on changes in the frequency with which they are used over time. With wider availability of large corpora reflecting many diverse genres of documents, it is now possible to take a broad and dynamic view of the importance of the proverb. Here, we measure temporal changes in the relevance of proverbs within four corpora, differing in kind, scale, and time frame: Millions of books over centuries; thousands of books over centuries; millions of news articles over twenty years; and billions of tweets over a decade. While similar methodologies abound lately, they have not yet been performed using comprehensive phraseological lexica (here, The Dictionary of American Proverbs). We show that beyond simple partitioning of texts into words, searches for culturally significant phrases can yield distinct insights from the same corpora. Comparative analysis between four commonly used corpora show that each reveals its own relationship to the phenomena being studied. We also find that the frequency with which proverbs appear in texts follows a similar distribution to that of individual words.
Introduction
Our goal here is to advance “computational paremiology”: The data-driven study of
proverbs, and in general to examine the utility of frequency-based studies of common
phrases in large corpora. In particular, we hope to answer the following: Can a
computational study of proverbs in large corpora offer unique insight in the study of
those proverbs? And is this novel kind of approach appropriate to corpus linguistics and
studies using corpora broadly? We first build a quantitative foundation by searching for
and counting instances of an ecology of proverbs, and estimating their frequency of use
over time in several large corpora from different domains. We then characterize basic
temporal dynamics allowing us to address fundamental questions such as whether or not
proverbs appear in texts according to a similar probability distribution to words [Zipf 2012]
[Balasubrahmanyan 1996] [Williams 2015b] [Cancho 2001].
In studies of phraseology, data on frequency of use is often conspicuously absent
[Čermák 2014]. The recent proliferation of large machine-readable corpora has enabled
new frequency-informed studies of words and n-grams (phrases of
length n) that have expanded our knowledge of language use in a
variety of settings, from the Google Books n-gram Corpus and the
introduction of “culturomics” [Michel 2011] [Pechenick 2015a], to availability and
analysis of Twitter data [Alshaabi 2020]. Routine formulae, or multi-word expressions
that cannot be reduced to a literal reading of their semantic components, remain
notoriously averse to reliable identification despite carrying high degrees of symbolic
and indexical meaning [Sag 2002]. It is, for instance, much easier to chart a
probability distribution of single words or n-grams than phrases
like proverbs, conventional metaphors, or idioms, which must be associated with a
lexicon – a set of meaningful linguistic units.
Studies of words alone can generally assume that each word is lexically represented as
such, but the study of phrases with a particular cultural use may require a lexicon in
addition to the text being studied. Here, we use Mieder’s Dictionary
of American Proverbs as such a resource. Assuming words or “grams” as
fundamental components of texts risks flattening the complex interrelations between
common phrases that might place a text in a historical or literary context, by
de-emphasizing or omitting altogether culturally significant phrases. We show a case
(proverbs) in which established computational methods may be applied to a class of
culturally meaningful phrases, with results that paint a valid and substantially unique
picture of language use in the corpora studied.
Perhaps the most recognizable routine formulae are proverbs and their close cousin,
idioms. Centuries of the study of proverbs – paremiology – have shown their importance
in language and culture, and that they are immensely popular among the folk (the people
for whom these phrases are culturally relevant) [Mieder 2012]. Proverbs are generally
metaphorical in their use, and map a generic situation described by the proverb to an
immediate context. In light of challenges in developing reliable instruments for
measurement and quantification of figurative language, research would greatly benefit,
as it has with words, from a better understanding of the frequency and dynamics of
proverb use in texts. By applying new methodologies in measuring frequency and
probability distributions, this study seeks to contribute to this endeavor.
Before going any further, we must detail a more precise definition of the proverb.
Though there is still some debate, it is widely agreed that proverbs are popular sayings
that offer general advice or wisdom. Naturally, not all such sayings are proverbs.
Mieder’s definition is perhaps the most useful for our present purposes: “Proverbs [are]
concise traditional statements of apparent truths with currency among the folk. More
elaborately stated, proverbs are short, generally known sentences of the folk that
contain wisdom, truths, morals, and traditional views in a metaphorical, fixed, and
memorizable form and that are handed down from generation to generation” [Mieder 2008].
Proverbs maintain a particular relationship with their context of use that provides a
fruitful domain for frequency and probability analysis. An important part of the proverb
is the context in which it is used. The metaphorical property of a proverb need not only
have to do with the proverb itself (as in the proverb/metaphor “war is hell,” in which
war is compared to hell within the proverb). In general, the use of a proverb is
metaphorical in context, meaning that the proverb offers wisdom about a current
situation via a metaphoric comparison to a proverbial one [Mieder 2008]. For instance,
while the proverb “still waters run deep” might be used to caution someone against
taking a seeming calm for granted, as it may belie unseen dangers. As with many other
proverbs, it is hard to imagine anyone using the proverb “you can’t put lipstick on a
pig” in any literal or pragmatic context. Rather, these phrases offer wisdom embodied in
the culture as opposed to that of the speaker. In this way proverbs may be used
generically without proffering personal expertise.
Proverbs are necessarily ambiguous enough to offer wisdom in any number of situations.
Michael Lieber argued that this ambiguity paradoxically gives proverbs the function of
disambiguating situations in which they are used. In part due to their role as cultural
rather than individual wisdom, they can be invoked impersonally as a way of clarifying a
complex reality [Lieber 1994]. As such, part of Winick’s definition of the proverb is
that they “address recurrent social situations in a strategic way” [Mieder 2008].
It is important to note the distinction between proverbs and idioms. An example of an
idiom would be the phrase “red herring” denoting a mislead. The meanings of idioms, like
proverbs, often cannot be ascertained from the meanings of their component words. But
unlike proverbs, idioms are often not complete sentences, require context, and need not
reference a paradigmatic situation. Proverbs on the other hand represent a complete
situation and offer some sort of general wisdom. The boundary between the two is rather
fuzzy and contains many idioms and proverbial expressions. For instance, the proverb
“every cloud has its silver lining” is perhaps more well known by its idiomatic
reduction “silver lining.” In fact, people may use an idiom without any knowledge of its
proverbial context. Our intent here is to focus on expressions of full proverbs, and not
their idiomatic uses. As previous work has shown, it is possible to investigate the
manipulations and idiomizations of individual proverbs [Čermák 2014] [Moon 1998]. Our
approach has limitations, and further research into flexible searches or other
identification methods will be essential in future work.
Metaphor and idiom identification and comprehension are an open area of research in
machine learning and NLP (Natural Language Processing) [Fazly 2009] [Shutova 2010]. In
general, metaphors and metaphorical speech are difficult to identify, and do not occur
in consistent, repeated phrasings. Whereas in the study of individual words, one is
allowed the tacit assumption that most of these words are represented in the lexicon of
the language, in the search for routine formulae, one must access the lexicon as an
essential step in verifying a phrase’s meaningfulness. Furthermore, the source and
target domains of their metaphoric mapping are seldom explicit, as laid out by Lakoff
and Johnson in their Conceptual Metaphor Theory [Lakoff 1985] [Andersson 2013]. Proverbs
generally appear in the same recognizable format, and in the form of a full,
self-contained sentence. Prospectively, understanding of the conceptual mapping involved
in proverb use may provide a useful step towards general understanding of metaphors in
the above fields [Özbal 2016a] [Özbal 2016b].
Arguably, the proverb’s flexibility of use has helped make them an essential part of
language and communication, literature, discourse, and media [Mieder 2012]. Interest in
the collection and study of proverbs dates back to at least the ancient Greeks and
Sumerians. Erasmus famously collected proverbs. In English literature, the proverb has
been an important device for many famous authors, among them Geoffrey Chaucer, William
Shakespeare, Oscar Wilde, and Agatha Christie [Abrahams 1994] [Obelkevich 1994].
Quantitative Approaches
This is by no means the first quantitative study of proverb use. Permiakov called for
demographic studies of proverb knowledge to gather an impression of which proverbs were
being used by the folk, in the interest of establishing a paremiological minimum: A
minimum lexicon of proverbs for understanding a language [Permiakov 1989]. Subsequent
interest in proverb knowledge in psychology and folklore resulted in several studies
conducted in the United States. Early studies by Albig and Bain in the 1930s found that
American college students could recall on average between 25 and 27 distinct proverbs,
many of which were common among participants [Albig 1931] [Bain 1939]. A more recent
study by Haas observed proverb familiarity among college students in several regions of
the US. They performed experiments in both proverb generation and proverb recognition.
Notably, students could recognize more proverbs than they could recall on their own
[Haas 2008].
Apart from the lexicographic collection of proverbs from texts, several attempts have
been made to quantify and characterize their use. Whiting, in his assiduous collection
of proverbs from texts in “Modern Proverbs and Proverbial Sayings” [Whiting 2014], kept
track of the frequency with which they were encountered. Norrick attempted a manual
search for proverb frequency, though he was constrained to only using proverbs starting
with the letter f, and used a relatively small text sample
[Norrick 1985]. In the first serious computational analysis of proverb frequency, Lau
searched for and counted instances of proverbs in newspapers in the Lexis/Nexis ALLNWS
database [Lau 1996].
David Cram theorized that proverbs, acting as self-contained lexical units, were
employed much in the same way that words are, and that their use involved a “lexical
loop” where the speaker accesses the lexicon in addition to the syntax when forming a
text. As such, in the case of proverbs (and phrasal idioms), one ought to “analyze a
syntactic string as a single lexical item” [Cram 1994].
Moon’s exhaustive early study of fixed expressions and idioms (denoted FEIs) in the
Oxford Hector Pilot Corpus (OHPC) did just that [Moon 1998]. His study represents the
first serious attempt to apply the new tools of computational linguistics to routine
formulae. He searched the OHPC (a precursor to the British National Corpus or BNC) for
instances of 6776 FEIs from the Collins Cobuild English Language Dictionary. It is worth
noting that at the time, there were few machine-readable English phraseological lexica.
Though proverbs consisted of only 3.5% of the searched phrases (240), 19% of the
expressions found in the corpus were proverbial expressions, the second most common
subtype behind “simple expressions” (70%). Of the proverbs found, 59% were deemed
metaphorical. Moon notes that exploitation of FEIs are easy to miss, and uses the
proverb “a bird in the hand is worth two in the bush” as an example.
Significantly, Moon noted that journalism was over-represented in the corpus, and that
the results did not represent the distributions of these FEIs in English as a whole.
This and other similar caveats inspired the present study to observe genre-specific
corpora separately, and compare after analysis.
Čermák’s essay collection Proverbs: Their Lexical and Semantic Features contains
several essays that deal with the distribution of proverbs in the British National
Corpus [Čermák 2014]. In Čermák’s pioneering essays, he searches for occurrences of
English proverbs in the BNC corpus (100 million words) [British National Corpus 2001].
In his study, even the most common proverbs seem to occur relatively infrequently. For
example, “easier said than done” is the most common, appearing 62 times in the entire
corpus. His study discusses the relevance of corpus occurrence to a paremiological
minimum (he uses a limited proverb list from Wiktionary). Another study focuses on text
introducers to various proverbs using collocation analysis. (Čermák notably
created/spearheaded one of the first machine-readable phrasaeological lexica in the
“Czech Idiom Dictionary” (1994).)
Čermák relates frequency dictionaries to discussions of a paremeological minimum. Should
proverb frequency in large corpora be considered when judging that minimum? Of course,
there are problems with this approach as well: proverbs rely heavily on oral tradition,
and are prone to frequent corruptions and purposeful exploitations. As such there is no
guarantee that a search of a given phrasing of a proverb will capture all, if any, of
its occurrences in a text. There are ways around this on an individual basis, but it
depends on the proverb: some employ parallel structures (like “good X make good Y”), or
have popular idiomizations (like “silver lining”). Longer proverbs are more likely to
appear in more than one form, as words and clauses can be swapped rearranged, or omitted
without changing their overall meaning, which makes computational identification more
difficult. Shorter proverbs will more reliably appear in a consistent form because there
are fewer words to manipulate, and any manipulation is likely to change their meaning.
In a recent introductory paremiology textbook, Steyer (2015) outlined a process general
corpus linguistic method for studying proverbs, similar to Moon and Čermák. Most
recently, Haas (2022) used Google Trends to analyze the frequency of Google searches of
proverbs included in discourse around the COVID-19 pandemic. Here, we expand on the
above literature, including much larger corpora and proverb data sets.
Should the ambition be to find these distributions in English as a whole? We contend
that there is no such universal corpus for any language. Clearly use of these phrases is
context-dependent, it seems unlikely inter-contextual searches will yield greater
insight than single-genre searches. Instead, frequency dynamics and distributions in
separate corpora from differing contexts may be more informative.
From Data on Language to Culture
Our present study of proverbs from a corpus linguistic point of view examines proverb
frequency using several methods that are well established in the study of words, though
rarely used in conjunction: namely the dynamics of frequency over time, and the
relationship between frequency and relative popularity (rank). We illustrate that moving
beyond words to the study of significant phrases provides worthwhile insight that cannot
be captured by word-based methods, and yet reproduces some of the expected behavior of
words.
One of the foundational achievements in the study of complex systems was Zipf’s
identification of scaling laws in language and other social phenomena [Zipf 2012].
Studies of scaling can help us understand the relationship between common and uncommon
observations, and are particularly suited to studies of language, which relies both on
the utility of some elements, and the specificity of others. A rank distribution
describes the relationship between the frequency of a word’s appearance, and its
resulting rank among all words in the text. Zipf’s law shows that there is an inverse
relationship between the frequency and rank of words in a text, and that the most common
20% of the unique words in a text account for 80% of overall word frequency. It was
first observed by Zipf that the rank distribution of words in a text follows a power law
F
r
=
c
r
-
α
,
where
r
is a word’s rank,
F
r
is its frequency, with
α
≃
1
. As early as 1996, natural language (in the context of computational
linguistics) was cited explicitly as an example of the recently coined “complex adaptive
systems” [Balasubrahmanyan 1996].
While primary interest here is paid to its appearance and seeming ubiquity in language,
the same class of distributions have been observed in phenomena across a wide range of
fields including physics, biology, psychology, sociology, urban studies, and engineering
[Clauset 2009] [Martínez-Mekler 2009].
One shortcoming noted in many evaluations of Zipf's law in text is that power law
scaling breaks down toward the tails of these empirical distributions, meaning that the
lowest ranked words do not seem to follow Zipf’s law. Recent work by Williams et al.
[Williams 2015b] showed that power law scaling holds over more orders of magnitude when
randomly partitioned phrases are used rather than individual words. That study also
suggested a refocusing of corpus linguistic attention from words to phrases as essential
elements of language. Further work by Williams et al. (2015a) suggested that changes in
scaling in Zipf distributions of large corpora can be attributed to text mining. Few, if
any, attempts have been made to apply Zipf's law to phraseological lexica.
With large amounts of newly digitized text, corpus linguistics and
lexicology/lexicography have seen renewed wider interest, and new results. Can these
methods be used to tell new stories that are of interest to those working in the
humanities? And in particular, how can that work embed itself into the existing wealth
of knowledge accrued by those disciplines. In this case, how can computational work on
proverbs situate itself in the existing knowledge-base of paremiology?
In their seminal 2011 paper, Michel et al. discussed the newly created Google Books
corpus, and coined the term “culturomics” to describe the nascent discipline concerned
with observable trends in the use of n-grams over time [Michel 2011]. They present several case studies, among them trends in the use of “influenza”
with historical outbreaks, and the use of geographical and antagonistic terms alongside
the history of the American Civil War. These case studies make use of time series data
and relative frequency to tell complex stories of interest from simple queries.
Pechenick et al. note that there are serious issues with assertions that Google Books
offers a reliable representation of culture. For one, books are not indexed by
popularity, and each book appears only once. As a result, the linguistic contributions
of the most popular books are weighted equally with the least popular [Pechenick 2015a].
Secondly, the increase in volume of scientific publications in the last century causes
the last century of English as a whole to be relatively skewed towards that genre. For
instance, enormously influential books like To Kill a
Mockingbird, I Know Why the Caged Bird Sings, Mockingjay, or Harry Potter and the Order of the
Phoenix are only represented once, and share the same weight as even the most
obscure books. In the last century, the rise in volume of scientific and academic
publication drastically increased the relative influence of this type of writing. Here,
we examine only the English Fiction subset of the corpus, which is a less problematic
subset [Pechenick 2015b].
Other work by Reagan et al. utilized the timelines within texts
to evaluate the “emotional arc” of a text, given word valence (sentiment) data.
Emotional arcs were created by plotting the changing sentiment of words in a text as it
progresses: from beginning to end, how positive or negative is the overall languge in a
given section? Inspired by Kurt Vonnegut's rejected Master's thesis (in anthropology) on
the shapes of stories, they found that the emotional arcs of most stories in the
Gutenberg corpus could be reduced to a handful of paradigmatic shapes [Reagan 2016].
Work by Underwood et al. used historical use of gendered names and words to reveal
trends in gender representation in literature using data from the HathiTrust digital
library [Underwood 2018].
StoryWrangler, a tool recently developed by Alshaabi et al. allows users to explore the
temporal dynamics of n-grams found on Twitter [Alshaabi 2020].
Using a data set reflecting a random 10% of Twitter since 2008 (presently over 150
billion tweets), Storywrangler tracks the prevalence of n-grams
on a daily scale. n-grams are portrayed via rank by popularity,
and convey the rise/dynamics of President Trump (further depicted in the PoTUSometer)
[Dodds 2021], or the meteoric rise, and continued influence of Justin Bieber (of
surprising relevance to this work). Unlike the Google Books n-gram Corpus, StoryWrangler is notable in its ability to track phrases in both
original tweets and retweets, conveying aspects of popularity through amplification.
Beyond simple words and phrases, data have been used to track the progression of ideas.
For instance, Leskovec et al.'s paper on “meme-tracking” tracked the progression and
mutation of popular sayings as they proliferated through news reporting and blogging
[Leskovec 2009].
Recently, “Computational Folkloristics” has gained recognition as an area of study, with
a 2016 issue of the Journal of American Folklore being devoted to
the subject [Tangherlini 2016]. Using classification, networks, geographical data,
temporal data, and digitized text, folklorists and other interested academics have
explored new possibilities in understanding texts and cultural history. The Danish
Folklore Nexus developed by Abello et al. provides tools for large-scale analysis of
Danish folk tales and stories, aiding in classification of stories, or mapping their
similarity to others through networks. Tools like this can augment traditional methods
of studying folklore, using data-driven methodology to guide future avenues of folklore
research [Abello 2012]. This represents a paradigmatic example of a computational tool
participating in the continued discourse around folklore, without being an end in and of
itself.
Data and Methods
In an effort to quantify the ecology of proverbial language, a list of over 14,000
proverbs was obtained from Mieder's Dictionary of American
Proverbs [Mieder 1992]. Proverbs were stored in an SQL database for ease of
access, and matched for frequency with four distinct corpora:
- The Gutenberg Corpus (English)
- The New York Times (1988-2007)
- The Google Books n-gram Corpus (1800-2000)
- Twitter (2008-2020)
For Google Books, the proverb “shit happens” was added to the set of proverbs to
illustrate the emergence of a modern proverb.
Individual corpora were collected as follows.
A. Gutenberg
The Gutenberg corpus comprises over 60,000 collected published documents spanning
several centuries. The present study restricts its use to the subset of documents in
English. As the metadata for the Gutenberg corpus does not consistently encode the date
of original publication, temporal data was collected using author birth dates (gathered
from the gutenbergr library for R) [Robinson 202]. These were used in place of
publication dates, as the publication dates in the corpus seldom represent the original
publication, instead they represent the digitized edition. For temporal analysis,
documents without authors and their birth dates were omitted.
The Gutenberg corpus comes with several caveats. Firstly, works were curated by
perceived importance. Works also disproportionately represent the 18th and 19th
centuries, and for this reason much of our work with Gutenberg focuses on this period.
Several authors have much of their extensive oeuvre represented in the corpus (e.g.,
Anthony Trollope, Mark Twain), which could compromise a more objective view of English
writing tendencies of the period.
B. The New York Times
Data from the New York Times were gathered from the New York Times Annotated Corpus of 1.8 million articles from
1987-2007 [Sandhaus 2008]. The data are organized in NTIF (News Industry Text Format)
formatted XML-readable documents. The corpus includes obituaries and other short pieces
in addition to more traditional news articles.
C. Google Books
The 2020 English Fiction Google n-grams corpus consists of every
n-gram that appears at least 40 times in its set of millions
of digitized books. For each n-gram the corpus provides on each
year it appears in the data set, the frequency with which it appeared that year, and the
number of documents it appeared in that year [Michel 2011]. Unlike Gutenberg and the New York Times, Google Books does not contain the raw text of the
concerned documents, rather it displays the counts for popular n-grams in the corpus, organized by n-gram length. This
creates an obvious limitation, where only phrases of the same length can be studied
together.
Data from Twitter was accessed through the Vermont Complex Systems Center's
StoryWrangler API [Alshaabi 2020]. StoryWrangler receives a randomly selected 1/10th of
each day's tweets from Twitter's Decahose API (including retweets), and organizes n-grams by rank and frequency. Data for 2-gram and 3-gram proverbs
were obtained though the tool, and were aggregated so the collection was case
insensitive. Similar to Google Books, this corpus is organized by n-gram counts rather than full texts.
D. Data Processing and Visualization
The data from all four corpora were processed using Python, and the libraries pandas and
matplotlib were used for organization and visualization respectively [pandas 2020]
[Hunter 2007].
In our processing of Gutenberg and the New York Times,
punctuation in both proverbs and texts was removed. Twitter data were punctuation
insensitive. Regular expressions were used to capture variations in punctuation when
processing the Google Books n-gram Corpus.
Estimating the frequency of a proverb’s use is essential to this work. Simple counts
don’t lend themselves to comparing results between corpora of different sizes, and do
not capture the frequency of the proverb in relation to the size of a single corpus.
Relative frequency can be calculated simply by dividing the raw frequency by a quantity
describing the size of a corpus (number of articles, number of n-grams, number of books, etc.) Expressed mathematically, it is calculated as:
f
r
e
l
=
f
t
/
n
t
which is the frequency
f
for time period
t
divided by the number of documents
n
found during time period
t
.
Zipf distributions were plotted using ranks of proverbs in a corpus, with rank 1 being
the most frequent against their frequency. Zipf distribution plots are shown on a
log-log scale as is standard, which displays less intuitive power law functions as more
intuitive linear functions. For results and analysis, see Appendix A.
A network approach will be useful in exploring how different books are connected by the
proverbs they share. In this case, a connection is drawn between two books if they share
at least one proverb. The resulting network emerges after this step is performed for
each possible pair of books. In network analysis, an important metric is centrality, which in this case describes how well-connected a book
is in the larger network. Specifically, we calculate betweenness
centrality, which assesses how often a given book appears in the shortest path
between any other two books in the network. A book with high betweenness centrality in
this network appears in the path between many pairs of books in the network. For results
and analysis, see Appendix B.
Betweenness centrality in these networks is calculated as
b
v
=
∑
s
≠
v
≠
t
σ
s
t
v
σ
s
t
,where
σ
s
t
denotes the number of shortest paths between books s
and t, and
σ
s
t
v
denotes the number of those paths that also pass through book v.
Most processing was performed using the Vermont Advanced Computing Core (VACC) located
at the University of Vermont.
Results
Gutenberg
While the most popular entry in the Gutenberg corpus and the Google Books n-gram corpus was the phrase “hold your tongue,” this phrase is
classified as a proverbial expression rather than a proverb (its use requires outside
context). For clarity of focus the phrase has been excluded from figures in this
section. “Sink or swim,” another proverbial expression, has been left in. In light of
the limitations of the Gutenberg corpus detailed in Methods, it is difficult to make
claims about the trends of proverb use over time (Figure 1). It is clear from the data
shown in Figure 1 that proverbs appear in a remarkable portion of the documents in the
corpus. “The sooner the better” for example, appears in nearly one in every ten
documents in the early 1800s.
Figure 1.
Time series for the 16 most popular proverbs in the Gutenberg corpus, ranked
by overall count. These most common proverbs occur in a large portion of documents in
the corpus for most of the period studied. For instance, “the sooner the better”
regularly appeared in at least 5% of documents from the 19th century. Plots are ordered
in the grid by rank first left to right, then top to bottom. Note that the vertical axis
ranges vary across plots to highlight individual variation in time.The New York Times
Figure 2 shows time series plots for the 16 most common proverbs in The New York Times Annotated Corpus. Shown are frequency binned by
month and year, and normalized by article count. All articles are included in the count
including smaller articles like obituaries (the average article count is 248 per issue).
It is by no means a surprise that proverbs appear frequently in journalism; in fact
Lau's study found as much [Lau 1996]. Not present in that work, is a temporal dimension
(not to mention a different time period). It is clear in Figure 2 that the proverbs
represented are used on a monthly or semi-monthly basis, and are rarely if ever absent
in a year's publications. In these representations of proverb use, it is easier to
identify use patterns and perhaps to extract narratives from their dynamics. The
easiest, if somewhat trivial case is “to delay may mean to forget” owes its yearly
rhythm to its role as the NYT's charity tagline. Its frequency of use increased markedly
over the period studied, though stayed confined to the winter holiday months.
Figure 2.
Time series plots for the 16 most popular proverbs in the New York Times from
1997-2007 (ranked by overall count). The gray represent the data binned by month, and
the orange represent the data binned by year. The proverb “to delay may mean to forget”
owes its yearly rhythm to its role as the NYT's charity tagline. The frequencies are
normalized by article count (obits, and non-body included). Plots are ordered in the
grid by rank first left to right, then top to bottom.With the exception of “to delay may mean to forget,” and consistent with accepted
definitions of the proverb, the consistency with which proverbs are used in the New York Times suggests they are employed widely for their utility
in mapping general wisdom to a specific context.
Nonetheless, prominent spikes in frequency can be associated with historical events. For
instance, the brief several-fold increase in the use of “boys will be boys” around
November of 1992 is likely attributed to a contentious and widely publicized sexual
assault case at the time, which prompted additional discussion of rape culture. Before
the trial, the president of the New Jersey chapter of the National Organization for
Women was reported as saying, “We're going to stop this 'boys will be boys' attitude
from continuing in this country.” Meanwhile in the trial, the lawyer for the defense
excused the rapists’ actions, telling the jury, “boys will be boys” [Glaberson 1992] [Hanley 1992].
The maximum in use of “pay as you go” in 2004 seems to correspond with discussion around
President Bush and the Republican party’s budget plans, referencing the “pay as you go”
budget policy from the 1990's by which tax cuts and spending increases must not increase
the defecit. Its increase in use in 1996 seems to owe to discussion of the Environmental
Bond Act being proposed in New York at the time, which proponents argued would cause
fewer delays than “pay as you go” funding for environmental clean-up [Vote Yes on the Bond Act 1996] [Henry 1996].
Google Books
In Figure 3 are time series plots for the 12 most common 2-gram proverbs in the Google
n-grams corpus. Here the gray represents yearly frequency
(counted once per volume), and the orange represents the five-year rolling average,
normalized by the number of volumes in a given year. One can see clearly from the figure
the emergence of several more recent proverbs: “safety first,” “money talks,” and “shit
happens.”
Figure 3.
Time series plots for the 12 most popular 2-gram proverbs in the Google Books
n-gram Corpus (ranked by overall count). The gray represent the yearly frequency, while
the orange represent the five-year rolling average. The dramatic increase in use of the
proverbs “shit happens” and “safety first” correspond with previous scholarship on their
emergence. Plots are ordered in the grid by rank first left to right, then top to
bottom.“Safety first” exhibits a precipitous rise in usage in the early 20th century.
Specifically, in 1912, the National Safety Council (NSC) in the US adopted the phrase as
its slogan to promote standards of worker safety, though the Safety First Movement was
initiated by US Steel in 1906. Its origin has been traced back to at least 1818 [Mieder 2019]. The data shown in Figure 3 support the history of its popularization [Swuste 2010] [Mieder 1992].
Previous scholarship on the proverb “shit happens” (which does not appear in The Dictionary of American Proverbs) traced its origin to the year
1944, and its rise in popularity corresponds to its humorous use as a bumper sticker,
and cultural controversy (and legal battles) associated with it [Mieder 2004] [Georgia 1991]. It’s increasing currency is illustrated in its appearance in Tom Clancy’s popular
novel, Clear and Present Danger: “Look, in field operations
anything can go wrong … We are not immune. Shit happens, as they say” [Clancy 1990]. It
also famously appeared in the movie Forrest Gump [Zemeckis 1994].
Figure 4 shows time series plots for the 16 most popular 3-gram proverbs in the Google
Books n-gram corpus. Though the proverb “never say never”
originated in 1887 [Mieder 1992], it is evident that it gained far wider popularity in
the late 1900s. Though the proverb “enough is enough” dates at least to 1546 [Mieder 1992], its popularity seems to vastly increase throughout the 20th century. The proverb
“divide and conquer” seems to have briefly gained popularity around the World War II
era.
Figure 4.
Time series plots for the 16 most popular 3-gram proverbs in the Google Books
Ngram Corpus (ranked by overall count). The gray represents the yearly frequency, while
the orange represents the 5 year rolling average. The rise in popularity of the proverb
“never say never” is shown. A period of increased usage of the proverb “divide and
conquer” corresponds with the World War II era. Plots are ordered in the grid by rank
first left to right, then top to bottom.On Twitter, the four most common 2-gram proverbs, on average, don't seem to exhibit much
variability in their usage (Figure 5). The proverbs “be yourself” and “time flies” seem
to remain above
10
-
6
, or 1 in every million 2-grams on Twitter during the period studied. An
increase in usage of “safety first” in early 2020 may be related to the onset of the
coronavirus pandemic during the same period.
Figure 5.
Time series plots for the nine most popular 2-gram proverbs on Twitter (ranked
by overall count). The gray represents the daily frequency, while the orange represents
the 30 day rolling average. The proverbs “be yourself” and “time flies” maintain
popularity over the period studied. Notably, the “safety first” shows an increase in
popularity in early 2020, possibly relating to the coronavirus pandemic. Plots are
ordered in the grid by rank first left to right, then top to bottom.Exhibited on Twitter (Figure 6), the convenience of proverbs as succinct narratives has
made them useful in several titular media events in the past decade. Of note, Figure 6
shows marked shifts in frequency of “never say never,” and “love is blind.” “Never say
never” owes its initial attention in 2010 to Justin Bieber's single of the same title
(Justin Bieber: Never Say Never), repeated as his slogan and
title of a biographical documentary. This was not the first film to utilize the proverb
in its title; Sean Connery's final performance as James Bond was titled Never Say Never Again (1983).
Figure 6.
Time series plots for 3-gram proverbs on Twitter (ranked by overall count).
The gray represents the daily frequency, while the orange represents the 30 day rolling
average. The proverb “never say never” owes its meteoric rise in popularity in 2010 to
popular musician Justin Bieber's single and biographical documentary of the same name.
“never say never” remains the most popular proverb on Twitter until 2016, when it is
supplanted by “enough is enough” which has steadily gained popularity in the last
decade, owed in part to its constant use by Senator Bernie Sanders, and punctuated by
reactions to tragedies related to gun and police violence. Plots are ordered in the grid
by rank first left to right, then top to bottom.Figure 7 shows the dynamics of “never say never” on Twitter in more detail. We observe
first its meteoric rise in popularity at the time of Never Say
Never's (song) release as the lead single off the soundtrack for a modern remake
of the Karate Kid movie (roughly two magnitudes in a single day).
At the time of the single's official release on June 8th, 2010, “never say never” was
the 63rd most used 3-gram on Twitter. When Justin Bieber: Never Say
Never was released on January 31, 2011, “never say never” was the 34th most
common 3-gram on Twitter; for comparison, “I love you” was 22nd at the time.
Figure 7.
Daily relative frequency of the 3-gram “never say never” on Twitter. While
“never say never” was already popular on Twitter as of 2008, its popularity was
amplified in 2010 by the release of Justin Bieber's single entitled “Never say never,”
and his subsequent biographical documentary of the same name. Remarkably, it remained
the most popular proverb on Twitter for almost six years, punctuated by anniversaries
and reruns of the movie, until it was surpassed by “enough is enough” in 2016.Remarkably, the popularity of “never say never” on Twitter decayed so slowly that it did
not reach its pre-Bieber frequency until 2016. The continued presence of the proverb in
Twitter discourse suggests that in the wake of its initial rise, it was more frequently
adopted to general non-Bieber usage. (A similarly popular 3-gram, non-proverbial song of
that year, “Rock That Body” appeared and disappeared from the Twitter discourse in the
span of a few months). While the enormity and fervor of Bieber's fanbase at the time (a
period called “Bieber fever” [Tweedle 2012]) certainly contributed to its popularity,
its continued use over a five-year period is compelling evidence that the proverb became
a more integral part of the Twitter lexicon for a time.
In 2020, “Love is Blind” became the title of a literally minded reality dating show in
which participants were quarantined in private rooms, only communicating via audio
interfaces. In this instance, the proverb was not only an apt description of the show's
narrative, but a template for its formation. Additionally, it came to represent a
narrative solution to the isolation imposed by the concurrent pandemic. The increase in
the phrase's popularity seems only to have lasted for the month of the show's release,
after which it seems to settle at its former rate of use. The proverb itself is ancient,
and translations exist nearly every European language.
While with “never say never” (the most popular proverb on Twitter), we see a sudden rise
and slow decay, we see a different pattern in the second most popular proverb, “enough
is enough.”
From 2016 to the present, we see a steady increase in the frequency of “enough is
enough” on Twitter (Figure 8). Recent work by Mieder attributes its renewed popularity
in part to its constant use by Bernie Sanders [Mieder 2019]. Unlike “never say never”
there does not seem to be a single event that precipitates this trend. An investigation
into the several local maxima (brief spikes in occurrence) suggests a possible narrative
correspondence. Many of these local maxima correspond to events related to either police
violence or mass shootings.
Figure 8.
Daily relative frequency of the 3-gram “enough is enough” on Twitter. The
popularity of “enough is enough” on Twitter grew steadily over the last decade, and it
has been the most popular proverb on Twitter since 2016, perhaps originating from its
consistent use by Senator Bernie Sanders [Mieder 2019]. It has since become associated
with growing protests against police brutality and gun violence. Annotations reflect
widely reported violent events and protests (with the exception of the 2018 US midterm
elections). The stark simplicity of this sixteenth century proverb evokes a narrative of
repetition past the point of tolerance [Mieder 1992]. In this instance, beginning as a
condemnation of the continued reaffirmation of the status quo in US politics by Senator
Sanders, it is now popular as collective outcry against political inaction in the wake
of regular mass shootings in the US, and a lack of accountability in the killing of
black Americans by police. The changing significance and popularity of the proverb in
the past decade displays the aptitude of proverbial speech to be successfully employed
in varying contexts, and its potential to illustrate narrative commonalities between
phenomena.Famously, survivors of the Parkland shooting in 2018 appeared on the cover of Time magazine with a simple title: “Enough.” [Alter 2018].
Coverage of the March for Our Lives against gun violence in the New
York Times included the title: March for Our Lives Highlights:
Students Protesting Guns Say “Enough Is Enough” [March for Our Lives 2018]. When
protesters marched in DC in the wake of the murder of George Floyd, Politico's coverage
was titled: “Enough is enough”: Thousands descend on D.C. for largest
George Floyd protest yet [Semones 202]. Inasmuch as proverbs can create
metaphorical mappings from a paradigmatic situation (or narrative) onto a present one,
“enough is enough” represents a compelling narrative of continued injustice, and a
critical point of retaliation. The data from Twitter display a narrative of repeated
tragedy in spite of public outcry. The proverb was most popular during the 2018 US
midterm elections, during which gun control was a major issue.
Concluding remarks
In this study, four corpora reveal four markedly different patterns of proverb use,
which taken each within the limits of their methods of collection, can offer piecemeal
insight into the relationship between phrases and the written record. Observing all four
side by side, it should become clear that generalizing linguistic tendencies from
individual corpora may not be a good idea. Even among corpora with significant temporal
overlap the results differ due to both the scope of each corpus, and the way in which
data are collected from texts. The present approach of studying data from distinct
domains allows for both a more limited and more useful interpretation of the results: We
can only claim that results are representative of proverb use on Twitter for instance,
rather than proverb use in English as a whole — an impossible achievement.
So, rather than using the results to speak about all proverbs or
all of language, we may use it as lens by which to identify areas (within a corpus) that
warrant a closer look. In studies of common words, the concept the word represents is
often dependent on its context, and evaluating context for each instance in a large
corpus is generally not feasible. Proverbs carry with them a paradigmatic context
Unlike studies of common words, whose interpretation is often dependent on their
context, we show that proverbs allow us to perform a different kind of analysis that
focuses on the historical use of a stable paradigmatic narrative.
While much work up to this point uses computational methods to take a broader view of
text than traditional methods of humanities scholarship, that broader view often
necessarily obscures the context in which linguistic elements are used. A single word or
short sequence of words may carry any number of meanings dependent on the words or
concepts around it. Proverbs carry their own paradigmatic context which makes their
meaning and use fairly consistent. While proverbs may be employed in many different
practical situations, the paradigmatic situation they represent remains the same. In
searching for proverbs, we are able to see how a paradigmatic context is applied to
different practical contexts. The results of this search show not only the frequency of
some sequence of words, but also the frequency with which a particular cultural concept
is employed.
N-gram based methods of analyzing common phrases in texts suffer
from the inclusion of many sequences of words which do not have any self-contained
meaning. Our work, in which phrases must be represented in the lexicon, ensures that
each phrase studied is meaningful. In an analysis of all 3-grams in a text, a very
common phrase without a fixed meaning, like “and this is” could obscure a far less
common meaningful phrase like “love is blind,” even though it is common among phrases
with fixed meanings. Our study of a specific cultural-linguistic phenomenon highlights
the utility of using an extensive lexicon to isolate the phenomenon being studied.
We demonstrate that lexically significant phrases (here proverbs), which are relatively
stable and self-contained, can discern trends that words or n-grams would likely miss. In each corpus studied, evidence of proverbs’ changing
use over time is shown to validate previous scholarship, reflect cultural events, and
offer a quantitative, longitudinal perspective unable to be achieved by traditional
methods in the study of proverbs.
The study of common and flexible phrases seems to lend itself to this mode of inquiry.
Through novel or context-specific words and phrases, we are able to observe discourse
around specific phenomena (“pizzagate,” “pandemic,” or “Make America Great Again”). In
contrast, through more generic culturally significant phrases, we may be able to observe
how we organize specific phenomena into the paradigmatic narratives they represent.
Much attention has been paid to the use of words and n-grams in
general in large corpora, but it is difficult to extract from them instances of
individual narrative or metaphorical language use. Proverbs, in their tendency to act as
both narrative and metaphor, and in their often relatively fixed structure, are an ideal
test case for our ability to observe broader cultural narratives through the piecemeal,
routine stories employed by the folk. Studies of n-grams tend to organize their
interpretation around n-grams which attain a specific historical use-case. In our study
of proverbs, we are able to trace the progression of a paradigm and its varying
application to historical changes and events.
A natural limitation of this study, and any study that uses extant data to study
language, is the issue of representativeness. In this study that limitation is twofold:
Both the lexicon for directing the search, and the data being searched are inherently
limited. While The Dictionary of American Proverbs is extensive,
and represents much that is known of proverbs in America, it naturally excludes new
proverbs and does not account for many ways in which the structure of the proverbs it
contains may be manipulated in their practical use. There are lexical resources that
address recent proverbs, for example The Dictionary of Modern
Proverbs, and the methodology of this study may be readily applied to such
lexica [Doyle 2012]. Previous studies on proverb frequency have relied on composite
corpora, namely variations of the BNC (British National Corpus), which contains manually
curated selections from several domains of text. As shown in this study, composite
corpora may miss important differences in proverb usage between domains.
Fieldwork (digital and otherwise) continues to be important in identifying new proverbs
and changing structures of existing proverbs. This task may be aided in the future by
tools like StoryWrangler, that track n-gram rank, likely
capturing new proverbs in the process. The task then would be extracting likely proverbs
from these data, which would require linguistic, cultural, and computational expertise.
Much of proverb scholarship has been concerned with the idea of a “paremiological
minimum”: A minimum proverbial lexicon for a language and culture. As shown by Lau
(1996), and again in the present study, computational studies of the frequency of
proverb use can contribute to the understanding of these minima, as those proverbs which
seem ubiquitous in large corpora ought to be understood by speakers of a language.
Temporal analysis of their frequency may further validate that their frequency is
related to enduring currency among the folk, rather than correspondence with a specific
occurrence. Another concern in paremiology and phraeseology is the origins of sayings.
Work like the present study can serve to both validate and expand on previous
scholarship on the history of phrases.
In the study of the statistical distribution of natural language, there exists the idea
of a kernel lexicon, a subset of words that are essential to communication using a given
language. Much literature on the study of culture and education has focused on what one
might consider a “minimum of cultural literacy”. Special attention has been paid to
which proverbs constitute part of that minimum. It is clear from this study that the
most common proverbs vary considerably between corpora. Given the prevalence of these
popular proverbs in their respective contexts, we can posit that English learners would
benefit in their comprehension of the language if they were familiar with these
proverbs.
Analyses of the frequency and rank of proverbs in this study (shown in Appendix A)
verify that with ever increasing amounts of machine-readable textual data, we may
produce longitudinal phraseological studies.
Traditionally, the study of metaphor in language has relied on theoretical
interpretations [Lakoff 1985]. This study opens up the possibility for new avenues in
the study of metaphor that incorporate the currency of specific metaphors among the
folk. A natural extension of this study would be employing a similar method to study
idioms and conventional metaphors.
As machine comprehension of natural language becomes increasingly important, this area
too, would benefit from an expanded lexicon that includes proverbs and routine formulae,
and understanding of metaphor may be assisted by a more basic understanding of the
mapping from general to specific situations that exists in the use of proverbs.
Acknowledgements
Thank you to David Dewhurst, Josh Minot, Michael Arnold, Nicholas Allgaier, Thayer
Alshaabi for providing invaluable guidance in the writing of this paper. The authors are
grateful for the computing resources provided by the Vermont Advanced Computing Core
which was supported in part by NSF award No. OAC-1827314, and financial support from the
Massachusetts Mutual Life Insurance Company to CMD and PSD.
Appendix A
Figure 9 shows Zipf distributions for entries from Mieder's Dictionary of American Proverbs (1992) for each of the four corpora studied,
using 3-gram proverbs for Google Books and Twitter. While the distributions exhibit some
Zipf’s Law-like behavior (heavy tails), we do not observe robust power-law scaling for
all proverbs. We find the largest number of distinct proverbs appearing in Gutenberg and
the New York Times, on the order of thousands, with the Google Books and Twitter
examples showing many fewer. We note that Zipf's law for words does not itself extend
over many orders of magnitude [Williams 2015a], typically only 2 or 3, and that it is
meaningful, mixed length phrases that present many orders of magnitude of scaling
[Williams 2015b]. The Zipf distributions for proverbs are thus comparable to what we see
for single words.
With a more sophisticated method of proverb detection, one that captures minor
variations in phrase structure, we would expect to see some adjustments to the Zipf
distributions we have observed, though a priori it is not clear how. Short, robust
proverbs (“time flies”) will be well counted, while longer ones for which, say,
constituent function words might be changed based on context or era (“he/she/they who
hesitates…”) would only see their apparent observed frequency of usage grow.
Figure 9.
Zipf distributions for entries from Mieder's Dictionary of American Proverbs
[Mieder 1992]. For each corpus, proverbs are enumerated and shown on logarithmic axes as
a function of rank, with “hold your tongue,” to “delay may mean to forget,” “time will
tell,” and “never say never” topping the charts in Gutenberg, NYT, Google, and Twitter
respectively. Each distribution exhibits heavy-tailed behavior, more prominently for
Gutenberg and NYT.Appendix B
The data for proverbs in the Gutenberg corpus were used to construct a network with
documents as nodes, connected if a given proverb appears in both documents. When
betweenness centrality was calculated for nodes in the network, surprisingly James
Joyce's Ulysses had the 14th highest centrality, close to several
dictionaries of proverbs and quotations, and the collected works of Mark Twain (Table
B-1). Creasy (2008) documented Joyce's use of proverbs in Ulysses
from a critical perspective, noting that they are often altered, and blend high and low
culture in the work. As Joyce uses many fewer proverbs than a comprehensive proverbial
dictionary, the book's centrality in this network implies that Joyce's use of proverbs
is far from arbitrary, and that his choice of proverbs is purposefully situated in the
broader context of English proverbial knowledge.
| Book | btwn centrality | |
| 1 | Dictionary of Quotations | 0.043048 |
| 2 | Familiar Quotations | 0.022821 |
| 3 | Dictionary of English Proverbs and Proverbial Phrases | 0.014274 |
| 4 | A Polyglot of Foreign Proverbs | 0.013061 |
| 5 | The Entire Project Gutenberg Works of Mark Twain | 0.013041 |
| 6 | French Idioms and Proverbs | 0.010083 |
| 7 | Roget's Thesaurus | 0.009785 |
| 8 | Webster's Unabridged Dictionary | 0.007978 |
| 9 | U.S. Copyright Renewals 1950 – 1977 | 0.006709 |
| 10 | The Project Gutenberg Complete Works of Gilbert Parker | 0.006278 |
| 11 | Proverb Lore | 0.006028 |
| 12 | Complete Project Gutenberg John Galsworthy Works | 0.003897 |
| 13 | Complete Project Gutenberg Works of George Meredith | 0.003660 |
| 14 | Ulysses | 0.003184 |
| 15 | The Historical Romances of Georg Ebers | 0.003168 |
| 16 | Familiar Quotations | 0.003007 |
| 17 | The Circle of Knowledge | 0.002886 |
| 18 | The Complete Poetic and Dramatic Works of Robert Browning | 0.002749 |
| 19 | Complete Project Gutenberg Oliver Wendell Holmes, Sr. Works | 0.002657 |
| 20 | Motion Pictures, 1960-1969: Catalog of Copyright Entries | 0.002578 |
Table 1.
The 20 most central books by betweenness centrality, from a network of books
connected by shared proverbs in Gutenberg. Notably, James Joyce's Ulysses appears alongside several proverb and quotations collections, and the
collected works of Mark Twain.Appendix C
Tables 2-4 show the total count of the 50 most popular proverbs in
their respective corpora.
| Proverb | Count | |
| 1 | hold your tongue | 2,284 |
| 2 | the sooner the better | 1,536 |
| 3 | be yourself | 739 |
| 4 | let bygones be bygones | 685 |
| 5 | time flies | 603 |
| 6 | alls well that ends well | 588 |
| 7 | one thing at a time | 580 |
| 8 | business is business | 534 |
| 9 | sink or swim | 531 |
| 10 | forgive and forget | 477 |
| 11 | take it or leave it | 436 |
| 12 | nothing is impossible | 421 |
| 13 | better late than never | 419 |
| 14 | every man for himself | 414 |
| 15 | know thyself | 394 |
| 16 | share and share alike | 372 |
| 17 | slow but sure | 363 |
| 18 | live and let live | 356 |
| 19 | the more the merrier | 352 |
| 20 | the die is cast | 348 |
| 21 | honesty is the best policy | 339 |
| 22 | to be or not to be | 335 |
| 23 | do or die | 322 |
| 24 | never say die | 319 |
| 25 | extremes meet | 289 |
| 26 | art for arts sake | 286 |
| 27 | all men are created equal | 265 |
| 28 | let well enough alone | 260 |
| 29 | time is money | 250 |
| 30 | no accounting for taste | 249 |
| 31 | peace at any price | 244 |
| 32 | tastes differ | 241 |
| 33 | history repeats itself | 235 |
| 34 | boys will be boys | 235 |
| 35 | charity begins at home | 231 |
| 36 | love is blind | 228 |
| 37 | the end justifies the means | 227 |
| 38 | one good turn deserves another | 224 |
| 39 | blood is thicker than water | 221 |
| 40 | not wisely but too well | 219 |
| 41 | all things work together for good | 213 |
| 42 | first come first served | 201 |
| 43 | keep the wolf from the door | 196 |
| 44 | dead men tell no tales | 195 |
| 45 | the wages of sin is death | 191 |
| 46 | seeing is believing | 187 |
| 47 | keep a stiff upper lip | 186 |
| 48 | ignorance is bliss | 185 |
| 49 | where theres a will theres a way | 183 |
| 50 | murder will out | 179 |
Table 2.
The top 50 proverbs and proverbial expressions (from the Dictionary of
American Proverbs) in the entire Gutenberg Corpus.| Proverb | Count | |
| 1 | to delay may mean to forget | 1,075 |
| 2 | enough is enough | 891 |
| 3 | time will tell | 864 |
| 4 | pay as you go | 597 |
| 5 | take it or leave it | 565 |
| 6 | do or die | 528 |
| 7 | first come first served | 463 |
| 8 | be yourself | 348 |
| 9 | father knows best | 307 |
| 10 | never say never | 276 |
| 11 | live and let live | 272 |
| 12 | money talks | 244 |
| 13 | the sooner the better | 240 |
| 14 | better late than never | 224 |
| 15 | sink or swim | 218 |
| 16 | boys will be boys | 213 |
| 17 | time flies | 205 |
| 18 | time is of the essence | 204 |
| 19 | divide and conquer | 198 |
| 20 | gentlemen prefer blondes | 192 |
| 21 | to be or not to be | 187 |
| 22 | the show must go on | 185 |
| 23 | time is money | 174 |
| 24 | talk is cheap | 167 |
| 25 | every man for himself | 166 |
| 26 | leave well enough alone | 163 |
| 27 | put up or shut up | 161 |
| 28 | business is business | 159 |
| 29 | accentuate the positive | 157 |
| 30 | forgive and forget | 151 |
| 31 | you get what you pay for | 142 |
| 32 | safety first | 142 |
| 33 | too little and too late | 140 |
| 34 | there is no easy way | 132 |
| 35 | let the chips fall where they may | 131 |
| 36 | all men are created equal | 129 |
| 37 | the more the merrier | 128 |
| 38 | history repeats itself | 122 |
| 39 | let bygones be bygones | 117 |
| 40 | one thing at a time | 113 |
| 41 | let nature take its course | 106 |
| 42 | never say die | 106 |
| 43 | seeing is believing | 102 |
| 44 | nothing is impossible | 100 |
| 45 | war is hell | 95 |
| 46 | the worst is yet to come | 85 |
| 47 | actions speak louder than words | 82 |
| 48 | gone but not forgotten | 82 |
| 49 | to each his own | 80 |
| 50 | let the buyer beware | 80 |
Table 3.
The top 50 proverbs and proverbial expressions (from the Dictionary of
American Proverbs) in the New York Times from 1987-2007.| Proverb | Count | |
| 1 | hold your tongue | 131,426 |
| 2 | time will tell | 65,640 |
| 3 | forgive and forget | 45,189 |
| 4 | enough is enough | 43,149 |
| 5 | business is business | 30,101 |
| 6 | sink or swim | 26,315 |
| 7 | nothing is impossible | 25,695 |
| 8 | easy does it | 23,655 |
| 9 | do or die | 21,672 |
| 10 | time is money | 18,856 |
| 11 | practice makes perfect | 17,469 |
| 12 | never say never | 16,649 |
| 13 | divide and conquer | 15,673 |
| 14 | love is blind | 14,439 |
| 15 | seeing is believing | 12,951 |
| 16 | never say die | 12,329 |
| 17 | ignorance is bliss | 11,838 |
| 18 | history repeats itself | 11,529 |
| 19 | fair is fair | 10,456 |
| 20 | slow but sure | 9,898 |
| 21 | forewarned is forearmed | 9,860 |
| 22 | love conquers all | 9,839 |
| 23 | misery loves company | 9,654 |
| 24 | facts are facts | 8,944 |
| 25 | time will pass | 8,389 |
| 26 | orders are orders | 7,620 |
| 27 | the truth hurts | 7,292 |
| 28 | blood will tell | 6,840 |
| 29 | father knows best | 6,783 |
| 30 | try anything once | 6,388 |
| 31 | murder will out | 6,349 |
| 32 | silence is golden | 6,278 |
| 33 | war is hell | 6,136 |
| 34 | business before pleasure | 5,811 |
| 35 | talk is cheap | 5,723 |
| 36 | revenge is sweet | 5,400 |
| 37 | familiarity breeds contempt | 5,095 |
| 38 | might makes right | 4,768 |
| 39 | consider the source | 4,677 |
| 40 | toe the mark | 4,549 |
| 41 | every little helps | 4,139 |
| 42 | time marches on | 4,019 |
| 43 | nothing is perfect | 4,007 |
| 44 | money is power | 3,757 |
| 45 | circumstances alter cases | 3,668 |
| 46 | respect your elders | 3,644 |
| 47 | gentlemen prefer blondes | 2,922 |
| 48 | mother knows best | 2,908 |
| 49 | love never fails | 2,848 |
| 50 | nobody is perfect | 2,801 |
Table 4.
The top 50 3-gram proverbs and proverbial expressions (from the Dictionary of
American Proverbs) in the Google Books Ngram Corpus.| Proverb | Count | |
| 1 | never say never | 2,549,095 |
| 2 | enough is enough | 2,182,460 |
| 3 | nothing is impossible | 978,533 |
| 4 | time will tell | 869,662 |
| 5 | the truth hurts | 748,285 |
| 6 | forgive and forget | 557,294 |
| 7 | talk is cheap | 465,608 |
| 8 | love is blind | 426,010 |
| 9 | practice makes perfect | 405,635 |
| 10 | nobody is perfect | 399,324 |
| 11 | time is money | 383,632 |
| 12 | ignorance is bliss | 377,037 |
| 13 | do or die | 316,328 |
| 14 | history repeats itself | 307,467 |
| 15 | love never fails | 255,795 |
| 16 | misery loves company | 226,217 |
| 17 | divide and conquer | 94,085 |
| 18 | facts are facts | 90,513 |
| 19 | respect your elders | 89,372 |
| 20 | seeing is believing | 86,169 |
| 21 | time will pass | 84,432 |
| 22 | silence is golden | 82,346 |
| 23 | love conquers all | 80,964 |
| 24 | revenge is sweet | 69,820 |
| 25 | health is wealth | 66,274 |
| 26 | never say die | 65,115 |
| 27 | prayer changes things | 63,757 |
| 28 | iron sharpens iron | 57,065 |
| 29 | sink or swim | 50,361 |
| 30 | tomorrow never comes | 50,297 |
| 31 | business is business | 39,525 |
| 32 | hold your tongue | 34,344 |
| 33 | nothing is perfect | 34,050 |
| 34 | try anything once | 33,370 |
| 35 | mother knows best | 26,848 |
| 36 | every little helps | 23,672 |
| 37 | never waste time | 22,244 |
| 38 | fair is fair | 18,125 |
| 39 | slow but sure | 14,404 |
| 40 | consider the source | 14,201 |
| 41 | justice is blind | 11,604 |
| 42 | money is power | 10,186 |
| 43 | time works wonders | 10,079 |
| 44 | time changes everything | 9,512 |
| 45 | like attracts like | 8,320 |
| 46 | familiarity breeds contempt | 8,166 |
| 47 | war is hell | 7,439 |
| 48 | easy does it | 6,071 |
| 49 | gentlemen prefer blondes | 5,273 |
| 50 | courtesy costs nothing | 3,890 |
Table 5.
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