# A diachronic evaluation of gender asymmetry in euphemism

Anna Kapron-King
Department of Computer Science
University of Toronto
<EMAIL_ADDRESS>
Yang Xu
Department of Computer Science
Cognitive Science Program
University of Toronto
<EMAIL_ADDRESS>

###### Abstract

The use of euphemisms is a known driver of language change. It has been
proposed that women use euphemisms more than men. Although there have been
several studies investigating gender differences in language, the claim about
euphemism usage has not been tested comprehensively through time. If women do
use euphemisms more, this could mean that women also lead the formation of new
euphemisms and language change over time. Using four large diachronic text
corpora of English, we evaluate the claim that women use euphemisms more than
men through a quantitative analysis. We assembled a list of 106 euphemism-
taboo pairs to analyze their relative use through time by each gender in the
corpora. Contrary to the existing belief, our results show that women do not
use euphemisms with a higher proportion than men. We repeated the analysis
using different subsets of the euphemism-taboo pairs list and found that our
result was robust. Our study indicates that in a broad range of settings
involving both speech and writing, and with varying degrees of formality,
women do not use or form euphemisms more than men.

##  1 Introduction

What role does gender play in language change and use? This question has long
been a matter of discussion among linguists. In Robin Lakoff’s influential
work on this topic, she proposes many ways in which language spoken by women
and about women differs from language used by and about men (Lakoff, 1973,
1977). Lakoff discusses the causes of these differences and what they tell us
about women’s role in society. The difference we focus on in this study is one
she mentions only briefly, namely that women use euphemisms more than men do
(Lakoff, 1977, p. 78). Lakoff is not the first to propose this difference.
Jespersen (2013, originally published 1922) also claims that women use
euphemisms more often, and discusses this supposed characteristic of women’s
language at length. Both Lakoff and Jespersen believe that women use
euphemisms more out of a desire to speak more tactfully and to avoid directly
mentioning “unladylike” topics. For example, Jespersen states that women have
often invented euphemisms to avoid mentioning “certain parts of the human body
and certain natural functions” (2013, p. 245).

Euphemisms have been considered as an important driver of language change
(Burridge, 2012). As a euphemism becomes conventionalized, it may become taboo
by its association with a taboo topic, and thus ends up being replaced by a
new euphemism. This process has been dubbed the euphemism treadmill by Pinker
(1994). While Lakoff (1977) considers euphemism use to be a sign of linguistic
conservatism, given what we know about the euphemism treadmill it may also be
reasonable to associate euphemisms with linguistic innovation. Since women are
thought to use euphemisms and invent new euphemisms in order to avoid taboo, a
finding that women do in fact use euphemisms more could be an indication that
women are leading the euphemism treadmill process.

One may take for granted that women use euphemisms more than men, because this
idea has been proposed by two renowned linguistics, and with a few decades
between them. However, both Jespersen and Lakoff base this claim primarily on
anecdotal evidence. To our knowledge, no one has attempted to quantitatively
evaluate whether greater use of euphemisms is characteristic of women’s
speech. Here we analyze euphemism usage through time by men and women in four
large text corpora of English to test this claim. Specifically, we examine
whether at a given time point in history, women use euphemisms with a greater
proportion in usage frequency than men do.

##  2 Related work

###  2.1 Defining euphemism

Definitions of euphemism can vary (Casas Gómez, 2009). The Oxford English
Dictionary (OED) defines a euphemism as “a less distasteful word or phrase
used as a substitute for something harsher or more offensive” (oed, 2020).
Euphemisms are a common part of everyday polite speech. For example, we might
describe certain bodily functions as “going to the bathroom” or when referring
to someone who has just died we might say they have “recently passed”.
Euphemisms can be used to discuss any taboo topic, without directly naming the
taboo. Taboo is culturally and contextually dependent, and as such so is
euphemism (Allan and Burridge, 2006; Burridge, 2012).

While there is variation in what different people consider to be a euphemism
in different contexts, people can judge words to be euphemistic without
needing context, such as when they compile euphemism dictionaries (Burridge,
2012). Allan and Burridge consider these judgements to be made following the
“middle class politeness criterion” (MCPC) (Allan and Burridge, 2006, p. 33),
which vaguely describes a polite, “middle-class environment” where a euphemism
might be preferred over a more offensive expression. This is roughly the
context we assume in this study.

Following the OED definition of euphemism, we consider only words and phrases
as euphemisms, though a euphemism could conceivably be any length of
utterance. We also assume here that a euphemism is an expression that
substitutes for a taboo expression, though some scholars argue that there is
not always a direct correspondence between a euphemism expression and a taboo
(Casas Gómez, 2009).

###  2.2 Gender and language

Throughout many different cultures and languages, it has been observed that
men and women use language differently, and some of these differences have
been remarked upon for centuries (Lakoff, 1973; Jespersen, 2013; Eckert and
McConnell-Ginet, 2013; Holmes, 1997; Coates, 2015; Labov, 1994). This
conversation surrounding gender and language has historically often been
framed as a characterization of “women’s language” (Jespersen, 2013; Lakoff,
1973). Gender differences have been proposed in a wide range of speech
characteristics, including word choice, sentence structure, topic choice, and
utterance length (Newman et al., 2008). In particular, Lakoff (1973) also
proposed that women’s speech is more “polite” than men’s, and this has been
discussed and studied extensively (Holmes, 1997; Brown, 1980; Newman et al.,
2008).

There have been many empirical studies of gender differences in language.
Newman and colleagues (2008) used text samples to comprehensively investigate
a large number of proposed gender differences in language use. Their results
did show evidence for some of these differences, but with small effect sizes.
Among some of their findings which supported existing claims were that women
use pronouns more than men, that men swear more than women, and that women use
polite forms (e.g., “Would you mind…”) more than men. Newman et al. (2008) did
not investigate gender differences in euphemism use, but since euphemisms are
often considered a form of polite speech (Allan and Burridge, 2006; Burridge,
2012), their positive finding that women use polite forms more than men may
lend credence to the idea that women use euphemisms more.

More recently, Park and colleagues (2016) studied the differences in topics
discussed by men and women on Facebook, and how these topics aligned with the
interpersonal dimensions of affiliation and assertiveness. They found that
women did not use more indirect language than men, contrary to the stereotype
that women are less assertive than men and contrary to some of Lakoff’s (1973)
claims about women’s language. Since euphemisms are a form of indirect speech
(Allan and Burridge, 2006; Burridge, 2012), this result could be seen to
provide evidence against the claim that women use euphemisms more than men.

###  2.3 Quantitative approaches to lexical semantic change and euphemism

There has been much interest recently in the field of computational
linguistics and natural language processing in applying quantitative methods
to historical language change, particularly semantic change Tahmasebi et al.
(2018). Existing work has explored aspects including but not restricted to the
automatic detection Sagi et al. (2011); Cook and Stevenson (2010); Kulkarni et
al. (2015); Schlechtweg et al. (2020), laws Xu and Kemp (2015); Hamilton et
al. (2016); Dubossarsky et al. (2017), and modeling Frermann and Lapata
(2016); Bamler and Mandt (2017); Giulianelli et al. (2020) of semantic change.
Differing from this line of work, our focus here is to understand the
formation and use of euphemism as a driver of language change. To our
knowledge, the closest quantitative approaches to euphemism sought to
automatically detect euphemism for content moderation Zhu et al. (2021) and to
classify phrases as euphemistic or dysphemistic using sentiment analysis Felt
and Riloff (2020), but there exists no quantitative work on characterizing the
role of gender in euphemism in a diachronic setting.

We utilize a set of 106 euphemism-taboo pairs and four large diachronic
corpora to test whether women use euphemisms with a higher proportion than
men. To verify the robustness of our results, we run the analysis on different
subsets of euphemism-taboo pairs to mitigate potential issues with our
selection of pairs. Throughout these analyses, we find no evidence that women
use euphemisms more than men over time.

In the following, we first describe the quantitative methodology we use to
investigate the claim that women use euphemisms more than men, and we then
discuss the results.

##  3 Methodology

We quantify euphemism usage by a proportion measure specifying how frequently
a given euphemism is used in natural language out of the sum of usage counts
of that euphemism and its corresponding taboo expression. This is how we
interpret Lakoff and Jespersen’s claim that women use euphemisms more. If they
only meant that women use euphemisms more without a higher euphemism
proportion, then their claim would be simply that women discuss taboo topics
more frequently than men, euphemistically or not, which we do not believe is
their intention. Hence, we evaluate whether women use euphemisms more by
testing whether they tend to have a higher euphemism proportion.

###  3.1 Diachronic text corpora

We analyzed four large diachronic text corpora covering different time
periods. We required corpora for which the author or speaker’s gender could be
determined for each data point. We chose to use longitudinal corpora because
euphemisms are known to change over time, and can often be short-lived
Burridge (2012), and as such we might expect the usage of a given euphemism to
change over time.

The corpora are: Reddit111https://github.com/ellarabi/gender-idiomatic-
language, New York Times Annotated Corpus (NYT)222Only available with
license., Canadian Parliamentary dataset (Canadian Parl.)333http://lipad.ca,
and United States Congressional dataset (US
Congr.)444https://data.stanford.edu/congress_text Rabinovich et al. (2020);
Sandhaus (2008); Beelen et al. (2017); Gentzkow et al. (2018). A summary of
statistics for these corpora is shown in Table 1. These corpora represent a
variety of registers; two of the corpora are spoken, three are formal, and one
is informal from social media. The NYT, Canadian Parl. and US Congr. likely
embody the MCPC context described by Allan and Burridge (2006), which makes
them good candidates for analyzing euphemism usage. Reddit is a less
controlled context, so it may not qualify for the MCPC, which makes it a good
point of comparison for the other three corpora.

| Reddit | NYT | Canadian Parliament | US Congress
---|---|---|---|---
timespan | 2006–2020 | 1987–2007 | 1951–2018 | 1951–2010
mean entries per year | 8,138,844 | 88,365 | 40,756 | 138,105
initial % entries by women | 28% | 9% | 0.4% | 0.6%
final % entries by women | 39% | 21% | 24% | 41%
Table 1:  A summary of basic statistics for each corpus used in this study.
The Canadian Parliament and US Congress datasets are available for earlier
years, but the year 1951 was chosen as their starting point because prior to
that year the data for women is too sparse.

We did not analyze more dated historical corpora for a few reasons. It would
be very difficult for us to judge what should be considered a euphemism 100 or
more years ago. We would also need data with a high enough proportion of women
authors such that we would not have data sparsity issues, and we expect more
recent datasets to have larger proportions of women. The article in which
Lakoff says women use euphemisms more was published in 1977 Lakoff (1977),
which falls within the time span of our analysis.

The US Congr., Canadian Parl., and NYT corpora have a large gender imbalance,
with only a small (but increasing) percentage of data each year having been
produced by women. We perform the analysis of the US Congr. and Canadian Parl.
data beginning in 1951, because from this year on the number of speeches by
women per year exceeds our chosen sample size of 100, as we describe later.

###  3.2 Euphemism-taboo pairs

In order to analyze the usage of euphemisms compared to taboo expressions on a
large scale, we need a data source which pairs euphemism expressions with
their equivalent taboos. For example:

  * •

passed away (euphemism) $\rightarrow$ died (taboo)

  * •

bust (euphemism) $\rightarrow$ breast (taboo)

While there are many euphemism dictionaries Neaman and Silver (1995); Rawson
(1981), and the online OED has a “euphemism” category by which to browse
dictionary entries, the entries in these references do not provide direct
correspondences between euphemisms and taboo expressions. They also tend to
include antiquated, overly specialized, and highly polysemous euphemisms. For
example, Neaman and Silver include a green hornet as a euphemism for a
motorcycle traffic policeman in Toronto, Canada (Neaman and Silver, 1995,
p.195). To our knowledge, there is no existing list of euphemisms paired with
taboo expressions. Our contribution includes a list of 106 pairs of euphemism
expressions and taboo expressions, and we have tried by our best judgment to
choose expressions found in North American English that are not overly
ambiguous or esoteric. Table 2 shows a subset of the pairs that we analyzed.
The complete list is available here:

https://github.com/annakin6/euphemism-gender

Some of the pairs come from articles which discuss a perceived societal
preference for one phrase over another Collier (2010); Hayes-Bautista and
Chapa (1987); Martin (1991); Nowrasteh (2017); O’Conner and Kellerman (2012);
Sagi et al. (2015); Silver (2015); Woelfel (2019); Yandell (2015), while
others we found in euphemism dictionaries and the OED Neaman and Silver
(1995); OED ; Rawson (1981). A remaining minority of pairs were determined
from our own knowledge of euphemisms. The euphemisms were chosen to represent
a variety of topics, such as illness, body parts, and war. Previous work has
shown that men and women tend to discuss different topics Newman et al.
(2008); Park et al. (2016), so it was important to choose topics that would
not favour only men or only women. The same expression sometimes appears in
this list as both a taboo and a euphemism (in different pairs). This is
because we have included pairs that represent different stages of the
euphemism treadmill.

euphemism | taboo | source
---|---|---
[late after line= |  |
, late after last line= |  |
Table 2: A subset of the lemmatized euphemism-taboo pairs used in this study,
with their corresponding sources (or – if no source could be identified).

###  3.3 Quantification of euphemism usage proportion

To determine euphemism use by gender, we first divided the data according to
the speaker’s gender. The Reddit data was already separated into self-reported
binary gender categories, but the other three corpora did not explicitly
contain this information (except for some rare entries in the US Congr.
dataset). To classify gender, we first used the speaker’s title (e.g., Mr.) if
it was clearly masculine or feminine. If the title could not be used, we
relied on the R gender package to determine gender from the speaker’s first
name. This package allows for gender retrieval given a first name and a birth
year range. Using this package, we created 40-year bins for every decade from
1930 to 1990, and we considered the birth year of a given author/speaker to
between 20 to 40 years before the decade that their article/speech was
produced. For example, the gender of a speaker from 1951 would be determined
from classification data for the birth years 1890–1930. Any texts for which a
binary gender classification could not be determined were discarded. For this
reason, 17% of the Canadian Parl. data was discarded, 0.04% of the US Congr.
data, and 46% of the NYT data.

After dividing by gender, we selected a random sub-sample of fixed size from
each gender (100 speeches for Canadian Parl. and US Congr., 1000
articles/posts for NYT and Reddit), to make up for the gender imbalance. For
each euphemism-taboo phrase pair we counted the number of times the euphemism
and the taboo expression occur in the sample. For each corpus and each pair,
we computed a euphemism usage proportion p for each gender, as shown in
Equation 1, where $f_{y}^{g}$ is the frequency of the expression in the sample
for gender g and year y.

(1)

To ensure plural, past tense, and other inflected forms of the euphemisms are
not overlooked, both the corpora and the euphemism-taboo pairs were lemmatized
using the nltk WordNetLemmatizer and part of speech tagger. This means that
each word is reduced to its base lemma, as informed by its part of speech
(POS) tag. For example, the word women, when its POS is noun, becomes woman,
and the word deprived, when its POS is adjective, remains deprived.
Lemmatization was used as it provided a more comprehensive and accurate
collection of euphemisms in various forms than a stemmer, which just removes
affixes, would. For example, the PorterStemmer would return women and depriv,
irrespective of POS.

##  4 Results

We first analyze for all euphemism-taboo pairs, the number of times they are
used by each gender, and for how many of these pairs women have a higher
euphemism proportion than men do. We then perform focused analyses on selected
subsets of the list of euphemism-taboo pairs. These analyses respectively
examine: only pairs where the euphemism or taboo is a multi-word phrase, and
only pairs which meet the cut-off threshold for all four corpora (i.e., the
conjunction set of euphemism-taboo pairs). We find in all the analyses that
women do not use euphemisms more than men.

Figure 1: Euphemism and taboo frequencies over time. The top row shows raw
euphemism frequency over time, averaged over samples and time spans (span = 2
years for Reddit and NYT, 10 years for Canadian Parl. and US Congr.). The
shaded area indicates a 95% confidence interval. The second row shows raw
taboo frequency over time, averaged over samples and time spans. The y-axes
for the first and second row are not fixed to the same scale, since they are
only meant to illustrate the relative difference in frequency for men and
women. The third row shows euphemism proportion over time [0,1], as defined in
Equation 1. The axis indicating year differs depending on the timescale of the
dataset.

To consider statistical variation in the analyses, we repeatedly sampled 25
times for each gender at each year for each corpus. To alleviate data
sparsity, we placed the counts for the US Congr. and Canadian Parl. in 10-year
bins, and placed the data for Reddit and NYT in 2-year bins. We also excluded
all pairs which did not meet a certain frequency threshold, to eliminate very
sparse, unreliable results. For each euphemism-taboo pair and each corpus, we
check that both the euphemism and the taboo appear at least once in 10% or
more of the 25 samples. If not, we omit that pair from the results for that
corpus. The number of pairs out of the 106 that meet the frequency threshold
for Reddit is 32, NYT is 80, Canadian Parl. is 35, and US Congr. is 34.

Using a one-tailed Welch’s unequal variances t-test, for each pair we compared
the euphemism proportions in all 25 samples between genders. We recorded the
fraction of pairs for which women had a significantly higher euphemism
proportion than men at $p<0.05$, and vice versa for men. If women use
euphemisms more than men, we would expect this to return a large percent of
pairs where woman have a higher euphemism proportion than men, and a smaller
percent of pairs for men.

Figure 1 shows the euphemism and taboo expression frequencies by gender over
time for the pair lady (euphemism) vs. woman. The top two rows show binned
frequencies, and the third row shows the euphemism proportion from Equation 1.
This pair is one that specifically relates to women, and that Lakoff (1973)
proposed and explained in detail. We see as expected that women say both lady
and woman more than men do. However, women do not say the euphemism lady with
a higher proportion than men do. In fact, we find that men use this particular
euphemism with a substantially higher proportion than women in three of the
four corpora for almost their entire time spans.

To summarize the euphemism proportion results, Figure 2 shows the percent of
pairs over time where either women have a significantly higher euphemism
proportion than men, men have a significantly higher euphemism proportion than
women, or neither gender has a significantly higher euphemism proportion.

Figure 2: Percentage share of pairs (out of all pairs that met the frequency
threshold for a given corpus) for which women have a significantly higher
euphemism proportion of usage frequency than men, men have a significantly
higher euphemism proportion than women, or neither gender has a significantly
higher euphemism proportion.

The individual euphemism-taboo pair plots and the euphemism proportion summary
plots show that in all corpora, across the entire time span of 1951–2018,
women do not lead in their euphemism-taboo usage proportion. The majority of
pairs show no clear leader between men and women. There are some euphemism-
taboo pairs where both expressions are said more frequently by women than men,
for example women say lady and woman far more than men do in all four corpora,
which is to be expected.

We also repeated this analysis with bins three times larger for each corpus to
see if data sparsity affected our results (6 years for Reddit and NYT, 30
years for Canadian Parl. and US Congr.). This increases the number of
euphemism-taboo pairs which surpass the frequency threshold for all four
corpora, but we found the results still hold that women do not have a
consistently higher euphemism proportion than men, and that most pairs show no
significant difference in euphemism proportion.

Although we tried not to include many ambiguous expressions, some of the words
in our euphemism-taboo pair list do contain multiple senses. For example, weed
could be euphemism for marijuana or it could refer to an unwanted plant in a
garden. Since multi-word phrases are less likely to have multiple senses, we
ran the same analysis on only those euphemism-taboo pairs which contain a
multi-word phrase. For example, the pair armed conflict and war. This analysis
does not completely handle ambiguity, since multi-word phrases can be
ambiguous and we still permit one of the expressions in the pair to be a
single word, however it does help mitigate the effect of expressions with
alternative non-euphemistic and non-taboo senses on our results. The results,
shown in Figure 3, generally support our finding from the complete analysis
that there is a minimal difference in how much men and women use euphemisms.
The Reddit and Canadian Parliament graphs show women using euphemisms more,
but due to the small sample size (10 pairs and 15 pairs respectively) this
result is not very reliable.

Figure 3: Percent of significant euphemism proportion in the phrase-based
analysis. Same as Figure 2 but considering only the euphemism-taboo pairs
where at least one of the two expressions is a multi-word phrase. Figure 4:
Percent of significant euphemism proportion in the conjunctive pairs. Same as
Figure 2 but considering only the euphemism-taboo pairs which meet the
threshold for all of the four corpora. Figure 5: Percent of time with
significantly higher euphemism proportion (using the same bins we have used
throughout, as described in Section 4). The left heatmap indicates for a given
(euphemism, taboo) pair for what percent of time women had a significantly
higher euphemism proportion for that pair than men did. Darker purple blocks
were higher for more time, empty blocks never had a significantly higher
euphemism proportion for women. The right heatmap shows the same for men, with
darker orange indicating a larger percent of time.

For the final analysis, we examine only the pairs which pass the sparsity
frequency threshold for every corpus. There are 15 such euphemism-taboo pairs
used in this analysis, the results for which are shown in Figure 4. We can
also visualize the amount of time for which each gender exhibits a
significantly higher euphemism proportion for each pair as a heatmap, shown in
Figure 5. The heatmap shows that there are no pairs for which women
consistently have a higher euphemism proportion than men for a larger period
of time across all four corpora. However, there are some pairs in some corpora
that stick out. The large percent of time for which women have a higher
proportion of saying weed compared to marijuana than men do in the NYT dataset
could likely be explained by the fact that weed is one of the more ambiguous
words in our set of 106 pairs and may be more commonly associated with
gardening than marijuana.

These results again support our finding that women do not use euphemisms more
than men. There are not many pairs where women have a higher euphemism
proportion for very long, and the pairs for which they do are not consistent
across corpora. There are, however, a few pairs where men consistently have a
higher euphemism proportion than women. For the pair lady-woman and the two
pairs containing breast, men prefer to use the euphemism more than women do,
across all four corpora.

##  5 Discussion

Our analysis of four large, varied datasets spanning 1951–2018 provides no
support for Lakoff and Jespersen’s claim that women use euphemisms more than
men do. This result means that we cannot assume that women use euphemisms
more. In general, we should not take for granted the characterization of
women’s language that has been proposed by linguists such as Jespersen and
Lakoff, as our study and others have shown that they are not always supported
by empirical evidence Newman et al. (2008); Park et al. (2016).

The result that women do not use euphemisms more indicates that euphemisms
should not be lumped in with other polite forms that women were found to use
more in other studies Newman et al. (2008). Our result is consistent with Park
et al.’s (2016) finding that women do not tend to use more indirect language
than men.

Our finding raises the question, why do Jespersen and Lakoff say that women
use euphemisms more if this is not actually the case? One reason women might
appear to use euphemisms more is that they may also talk more about certain
taboo topics. For example, in our results women say both chest and breast more
than men do, but men say chest with a higher proportion than women. The topic
of the euphemism seems to have some effect on whether men or women use it
more, though at a glance these do not seem to correspond to topics that have
been found to be discussed more by one gender or the other. There are of
course euphemisms that women do prefer to use more than men do, at least
within a certain context, such as women on Reddit preferring to say pass away
over die more than men. The claims made by Jespersen and Lakoff could be due
to generalizing from specific cases similar to this one. It is also likely
that gender differences in language have changed since Jespersen’s time – we
were unable to investigate this due to data sparsity and a lack of sources for
euphemisms from the 1920s. Even so, our corpora spanned 1951–2020 and our
finding was consistent throughout that time period.

The four corpora used in this study were chosen because we needed large
diachronic corpora for which the author’s gender could be approximately
determined. However, there are limitations to using these corpora. The
language used in political proceedings and in newspapers may be regulated by
political parties or the newspaper editors, which might minimize gender
differences in language in these corpora. The Reddit data only included posts
where users had self-reported gender, which may limit the topics that are
included. We did not include any natural conversation data, although that is
likely the setting that Lakoff and Jespersen were most concerned with.

There are also limitations to our selected list of euphemism-taboo pairs. The
list is relatively small, and was gathered manually. This list does not
represent all taboo topics, nor all types of euphemisms. However, we believe
it serves as a good first step for quantitative studies of euphemism. Future
work on automatic euphemism detection may allow us to generate a more
comprehensive list which should help with analyzing euphemism use over time
and other related phenomena.

Our study did not directly examine whether women lead euphemism innovation and
change, although our result does indicate that this is not likely to be
generally true. Future work could investigate who leads the formation of new
euphemisms and who drives the euphemism treadmill, while considering that the
answer to this question is likely context- and topic-dependant.

##  6 Conclusion

The subject of how women’s language and men’s language differ is one that has
been extensively discussed, and one alleged difference is that women use
euphemisms more than men do. However, this claim has been based on anecdotal
evidence. Our diachronic evaluation using large corpora spanning multiple
decades from a variety of contexts shows that women do not use euphemisms more
than men do. Our work indicates the importance of using quantitative methods
to evaluate long-held beliefs about language use and language change.

## References

  * oed (2020) 2020\.  euphemism, n. In _OED Online_. Oxford University Press.
  * Allan and Burridge (2006) Keith Allan and Kate Burridge. 2006.  _Forbidden words: Taboo and the censoring of language_.  Cambridge University Press.
  * Bamler and Mandt (2017) Robert Bamler and Stephan Mandt. 2017.  Dynamic word embeddings.  In _Proceedings of the 34th International Conference on Machine learning_.
  * Beelen et al. (2017) Kaspar Beelen, Timothy Alberdingk Thijm, Christopher Cochrane, Kees Halvemaan, Graeme Hirst, Michael Kimmins, Sander Lijbrink, Maarten Marx, Nona Naderi, Ludovic Rheault, Roman Polyanovsky, and Tanya Whyte. 2017.  Digitization of the Canadian parliamentary debates.  _Canadian Journal of Political Science_ , 50(3):849–864.
  * Brown (1980) Penelope Brown. 1980.  _How and why women are more polite: Some evidence from a Mayan community_. Preager Publishers.
  * Burridge (2012) Kate Burridge. 2012.  Euphemism and language change: The sixth and seventh ages.  _Lexis – Journal in English Lexicology_ , (7).
  * Casas Gómez (2009) Miguel Casas Gómez. 2009.  Towards a new approach to the linguistic definition of euphemism.  _Language Sciences_ , 31(6):725–739.
  * Coates (2015) Jennifer Coates. 2015.  _Women, men and language: A sociolinguistic account of gender differences in language_ , 3 edition.  Routledge.
  * Collier (2010) Roger Collier. 2010.  Who you calling obese, Doc? _Canadian Medical Association Journal_ , 182(11):1161–1162.
  * Cook and Stevenson (2010) Paul Cook and Suzanne Stevenson. 2010.  Automatically identifying changes in the semantic orientation of words.  In _Proceedings of the 7th International Conference on Language Resources and Evaluation_.
  * Dubossarsky et al. (2017) Haim Dubossarsky, Daphna Weinshall, and Eitan Grossman. 2017.  Outta control: Laws of semantic change and inherent biases in word representation models.  In _Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing_ , pages 1136–1145.
  * Eckert and McConnell-Ginet (2013) Penelope Eckert and Sally McConnell-Ginet. 2013.  _Language and gender_ , 2 edition.  Cambridge University Press.
  * Felt and Riloff (2020) Christian Felt and Ellen Riloff. 2020.  Recognizing euphemisms and dysphemisms using sentiment analysis.  In _Proceedings of the Second Workshop on Figurative Language Processing_ , pages 136–145, Online. Association for Computational Linguistics.
  * Frermann and Lapata (2016) Lea Frermann and Mirella Lapata. 2016.  A bayesian model of diachronic meaning change.  _Transactions of the Association for Computational Linguistics_ , 4:31–45.
  * Gentzkow et al. (2018) Matthew Gentzkow, Jesse M. Shapiro, and Matt Taddy. 2018.  Congressional record for the 43rd-114th Congresses: Parsed speeches and phrase counts.
  * Giulianelli et al. (2020) Mario Giulianelli, Marco Del Tredici, and Raquel Fernández. 2020.  Analysing lexical semantic change with contextualised word representations.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 3960–3973, Online. Association for Computational Linguistics.
  * Hamilton et al. (2016) William L. Hamilton, Jure Leskovec, and Dan Jurafsky. 2016.  Diachronic word embeddings reveal statistical laws of semantic change.  In _Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 1489–1501, Berlin, Germany. Association for Computational Linguistics.
  * Hayes-Bautista and Chapa (1987) David E. Hayes-Bautista and Jorge Chapa. 1987.  Latino terminology: Conceptual bases for standardized terminology. _American Journal of Public Health_ , 77(1):61–68.
  * Holmes (1997) Janet Holmes. 1997.  Women, language and identity.  _Journal of Sociolinguistics_ , 1(2):195–223.
  * Jespersen (2013) Otto Jespersen. 2013.  _Language: Its nature and development_, chapter 13. Routledge.  Original work published 1922.
  * Kulkarni et al. (2015) Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, and Steven Skiena. 2015.  Statistically significant detection of linguistic change.  In _Proceedings of the 24th International Conference on World Wide Web_.
  * Labov (1994) William Labov. 1994.  _Principles of linguistic change_ , volume 2 of _Language in Society_.  Blackwell.
  * Lakoff (1973) Robin Lakoff. 1973.  Language and woman’s place.  _Language in Society_ , 2(1):45–80.
  * Lakoff (1977) Robin Lakoff. 1977.  You say what you are: Acceptability and gender-related language.  In Sidney Greenbaum, editor, _Acceptability in Language_. De Gruyter Mouton.
  * Martin (1991) Ben L. Martin. 1991.  From Negro to Black to African American: The power of names and naming.  _Political Science Quarterly_ , 106(1):83–107.
  * Neaman and Silver (1995) Judith S. Neaman and Carole G. Silver. 1995.  _The Wordsworth book of euphemism_ , 2 edition.  Wordsworth.
  * Newman et al. (2008) Matthew L. Newman, Carla J. Groom, Lori D. Handelman, and James W. Pennebaker. 2008\.  Gender differences in language use: An analysis of 14,000 text samples.  _Discourse Processes_ , 45(3):211–236.
  * Nowrasteh (2017) Alex Nowrasteh. 2017.  The use of euphemisms in political debate.  _Cato Institute_.  https://www.cato.org/blog/use-euphemisms-political-debate.
  * O’Conner and Kellerman (2012) Patricia T. O’Conner and Stewart Kellerman. 2012.  Crippled, handicapped, disabled?  _Grammarphobia_.  https://www.grammarphobia.com/blog/2012/07/crippled-handicapped-disabled.html.
  * (30) OED. 1989.  OED Online.  http://www.oed.com/.  Accessed: 2020-04-06.
  * Park et al. (2016) Gregory Park, David Bryce Yaden, H. Andrew Schwartz, Margaret L. Kern, Johannes C. Eichstaedt, Michael Kosinski, David Stillwell, Lyle H. Ungar, and Martin E. P. Seligman. 2016.  Women are warmer but no less assertive than men: Gender and language on Facebook.  _PLOS ONE_ , 11(5):e0155885.
  * Pinker (1994) Steven Pinker. 1994.  The game of the name.  _The New York Times_.
  * Rabinovich et al. (2020) Ella Rabinovich, Hila Gonen, and Suzanne Stevenson. 2020.  Pick a fight or bite your tongue: Investigation of gender differences in idiomatic language usage.  In _Proceedings of the 28th International Conference on Computational Linguistics_.
  * Rawson (1981) Hugh Rawson. 1981.  _A dictionary of euphemisms & other doubletalk_, 1 edition.  Crown Publishers, Inc.
  * Sagi et al. (2015) Eyal Sagi, Timothy M. Gann, and Teenie Matlock. 2015.  The moral rhetoric of climate change.  In _Proceedings of the 37th Annual Conference of the Cognitive Science Society_ , page 2063–2068.
  * Sagi et al. (2011) Eyal Sagi, Stefan Kaufmann, and Brady Clark. 2011.  Tracing semantic change with latent semantic analysis.  _Current methods in historical semantics_ , 73:161–183.
  * Sandhaus (2008) Evan Sandhaus. 2008.  The New York Times annotated corpus.
  * Schlechtweg et al. (2020) Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi. 2020.  Semeval-2020 task 1: Unsupervised lexical semantic change detection.  _arXiv preprint arXiv:2007.11464_.
  * Silver (2015) Mark Silver. 2015.  If you shouldn’t call it the Third World, what should you call it?  _NPR.org_.  https://www.npr.org/sections/goatsandsoda/2015/01/04/372684438/if-you-shouldnt-call-it-the-third-world-what-should-you-call-it.
  * Tahmasebi et al. (2018) Nina Tahmasebi, Lars Borin, and Adam Jatowt. 2018.  Survey of computational approaches to lexical semantic change.  _arXiv preprint arXiv:1811.06278_.
  * Woelfel (2019) Mariah Woelfel. 2019.  Pot? weed? marijuana? what should we call it?  _WBEZ_.  https://www.npr.org/local/309/2019/09/19/762044859/pot-weed-marijuana-what-should-we-call-it.
  * Xu and Kemp (2015) Yang Xu and Charles Kemp. 2015.  A computational evaluation of two laws of semantic change.  In _Proceedings of the 37th Annual Meeting of the Cognitive Science Society_.
  * Yandell (2015) Kay Yandell. 2015.  Indian, Native, Indigene: The reverberations of a quiet linguistic revolution.  _American Literary History_ , 27(2):374–391.
  * Zhu et al. (2021) Wanzheng Zhu, Hongyu Gong, Rohan Bansal, Zachary Weinberg, Nicolas Christin, Giulia Fanti, and Suma Bhat. 2021.  Self-supervised euphemism detection and identification for content moderation.  _arXiv preprint arXiv:2103.16808_.