Gender, Sexuality and Bad Language
Tony McEnery, Department of Linguistics and
Modern English Language, University of
Lancaster
The background to this talk
• Work at Lancaster (Paul Baker, Andrew Hardie,
Neil Millar) supported by a grant from the
University
• Lancaster Corpus of Abuse (LCA)
• Published in part in ‘Swearing in English’
(2005) and a number of journal articles.
• Why - well there has been corpus based
studies of swearing (Leech, Stenstrom,
Ljung)
• The main studies of swearing remain noncorpus informed:
– Slang (Partridge, 1960)
– Anatomy of Swearing (Montagu, 1967, 1973)
– Female Eunuch (Greer, 1970)
– Language and Woman’s Place (Lakoff, 1975)
– Swearing (Hughes, 1991, 1998)
• All of these studies make claims about this
form of linguistic behaviour which is
amenable, to lesser or greater degrees to
corpus study
• In this talk I will focus on a few claims made
by Hughes by way of illustration and then
move on to examine gender related work
• But first …..
How are we doing it?
• Using categorisations used by others to
develop an annotation of all of the ‘swear’
words in the BNC spoken corpus(LCA 1.0) and
later a broader set of words (LCA 2.0)
• Some studies have not considered various
forms of swearing (e.g. swear words in a
premodifying position) so I developed a
categorization of these
• Used Sara to mine data from the BNC using
three variables as our search parameters sex, age, social class. Work had to be
redone as corpus was corrected.
• Of the three variables, the last was and
remains problematic
• Each word is then encoded to indicate the
age, sex and social class of the speaker
amongst other things
• Further annotation is added to reveal
Field Feature
Possible values
1
Gender (sp)
M = male, F= female X = unknown
2
Social class (sp) As per social class categories of BNC (see
Aston & Burnard, 1998)
3
Age (sp)
As per age categories of BNC (see Aston
& Burnard, 1998).
4
Category
As per table following
5
Gender (he)
As per gender of speaker
6
Person (target) 1 = first person, 2 = second person, 3 =
third person, X = unknown
7
Metalinguistic 0 = no, 1 = yes
8
Animacy
+ = animate, - = non-animate, X =
(target)
unknown
9
Gender (target) As per gender of speaker
10
Number (target) 1 = singular, 2 = plural, X = unknown
11
Quotation
Q = quotation, N = non-quotation, X =
unknown
Category
Personal
Personal by
reference
Destinational
Cursing
Label
Description
P
A second person
insult of the form
"You X" or
similar
R
A third person
insult of the form
"The X" or
similar
D
Swear word
followed typically
by off
C
An insult of the
form "X you" or
similar.
G
An imprecation
with no particular
target.
General
expletive of
anger,
frustration or
annoyance
Explicit
E
expletive
of
anger,
frustration or
annoyance
An imprecation
with a specific
target of the form
"X it!" or similar
Example
Yeah but Jonesy
ain't here you
cunt so it's only
me.
fucking serves
the cunt right as
well
Oh Jake sod off.
Can't even come
and say hello, so
bugger him!
Oh shit , I
haven't bought
any scissors!
Liz got quite
cross you know
she's quite oh
bugger it
There is plenty to do!
 Let’s
quickly look at some claims made by
Hughes, then move on to look at gay, queer,
puff and fuck. First Hughes:
 Claim one - the categories of swearing.
Which words fall into which categories
 Claim two - which words are used to insult
which sex
P
*
cunt
*
shit
*
fart
bugger *
bastard *
arsehole *
R
*
*
*
*
*
*
D
0
0
0
*
0
0
C
0
0
0
*
0
0
G
0
*
0
*
0
0
E
0
0
0
*
0
0
Term
Category (Our data)
P R D C G E
* * 0 0 0 0
cunt
* * 0 0 * 0
shit
* * 0 0 0 0
fart
* * * * * *
bugger
bastard * * 0 0 + 0
arsehole 0 * 0 0 + 0
Male targets only
Female targets only
Male targets only
Female targets only
Targets may be of either sex
No example of the word found as
personal insult in the LCA
Prick, cunt, twat, pillock, tit,
arsehole, shit, turd fart, idiot,
imbecile, moron, cretin, prat, swine,
pig
Cow, bitch, sow, fucker
Prick, cunt, pillock, shit, moron,
Twat, arsehole, fart, idiot, prat, cow,
bitch, swine, pig, bastard, bugger,
sod
Tit, turd, imbecile, cretin, sow, fucker
Looking at infrequent words
• Some of the word forms we are looking for have
a relatively low frequency in he corpus
• Words related to sexual orientation are such
words
• The reasons for this are interesting to consider
• Though small, the data sets may give interesting
suggestions which may be followed up by web as
corpus studies, for example
Gays, queers and poofs
• Data is sparse
• But even on a small scale the data is
interesting
• Gay (24 examples)
• Collocates: Is (10), He’s (9), You’re (2),
Dad’s (1), Who’s (1)
• A prosody of attribution in nearly all of the
cases (21)
• Strong colligation with the “X is gay”
pattern.
• The X is male:
• He’s (9), he (3), chap (1), dad’s (1), Mick (1),
James (1), Male (1), Pat (1), Phil (1), sons (1)
• Interestingly, no personal attributions of
being gay.
• Queer (3 examples)
• One abusive, but two have negative
attributions!
• Similar pattern of colligation, but negation
included
• “X is not queer”
• Is it that we are abusive of that we claim we
are not?
• Poofter (6 examples) & poof (2 examples)
• Singular common nouns. Always P abuse.
• Not used in an attributive manner
Sexual orientation
Positve/neutral prosody
Attribution
gay
Nominal ?
Negative prosody
Attribution
gay ???
Abuse/Swearing
Nominal
queer
Queer, puff,
battyman
Queer –
others??
• But note here that we are within a
heterosexual (or at least nominally
heterosexual) discourse community. This
pattern could clearly change if we shift to a
homosexual discourse community.
• The data is insufficient to test the
hypotheses, but it is a useful spur to the
flank of the analyst, and can set a research
agenda to be pursued by other means
Looking at more frequent words
• Some of the words we are looking at have a
frequency which means we can fully exploit
the annotation on the corpus with some
confidence
• Fuck is a good example of such a word
• So let’s look at fuck
Male v. Female - word forms
Form
Root
-ing
-ed
-er
-s
-ers
Total
Male
197
982
18
8
3
2
1210
Female
76
226
12
2
2
0
318
•Note that while quantity differs, ranking and proportions remain
fairly stable. So while swearing may differ quantitatively, it does not
differ qualitatively. Same is true of marked female words, like shit.
Full Categories
Code
Description
A
Predicative negative adjective: “the film is shit”
B
Adverbial booster: “Fucking marvellous” “Fucking awful”
C
Cursing Exp letive: “Fuck You!/Me!/Him!/It!” (retained LCA 1.0 category)
D
Destinational usage: “Fuck off!”
“He fucked off” (retained LCA 1.0
category)
E
Emphatic adverb/adjective: “He fucking did it” “in the fucking car”
F
Figurative extension of literal meaning: “to fuck about”
G
General explet ive “(Oh) Fuck!” (retained LCA 1.0 category)
I
Idio matic ‘set phrase’: “fuck all” “give a fuck”
L
Literal usage denoting taboo referent: “We fucked”
M
Imagery based on literal meaning: “kick shit out of”
N
Premodifying negative adjective: “the fucking idiot”
O
P
‘Prono minal’ form with undefined referent: “got shit to do”
Personal insult referring to defined entity: “You fuck!” / “That fuck”
(retained LCA 1.0 category)
R
‘Reclaimed’ usage – no negative intent
T
Relig ious oath used for emphasis: “by God”
X
Unclassifiable due to insufficient context
Categories
type
A
B
C
D
E
F
G
I
L
M
N
O
P
R
T
M
53
202
57
70
1131
190
799
234
58
69
413
19
403
9
19
F
Tendency
52
115
53
45
822
200
1250
225
90
44
517
20
359
13
35
M
M
M
M
M
F
F
M
F
M
F
F
M
F
F
Swearing in reported speech
Quotation
Yes
No
Male
118
3790
Female
219
3976
•Why is the relative proportion of reported uses of fuck higher
for females (roughly one sixth of examples as opposed to one
thirty fifth?)
Targets
Target below, Male Female
speaker across
Male
300 43
Female
40
73
•Proportionately, more female uses of fuck are aimed at females
than male uses of fuck, and more male uses of fuck are aimed at
males than female uses of fuck. We seem to swear at our own sex
most frequently.
Keywords
Gender
Male
Female
Keyword Negative
keyword
fucking Said, she
Said, she, Of, its, the
her,
old, a, he’s,
because, as, they,
bloody
first
cigarette
•Notice we have evidence for a tentative explanation of the
reported speech discrepancy
New Work – US Speech
• Longman Corpus of Spoken American English
(Du Bois for Longman)
• Work undertaken with Neil Millar
• Approximately 5,000,000 words of
orthographically transcribed spontaneous
speech
female
3681
male
4585
unknown
3399
Word
fuckage
fucked
fucker
fuckers
Fuckery
fuckhead
fuckheads
fuckin
Fucking
fucks
F
1
17
8
2
M
2
86
31
12
3
1
9
168
1
40
444
2
lemma
female
male
shit
fuck
god
gosh
damn
hell
gee/geez
ass
goodgrief/goodness/gracious/heavens
379
255
795
404
179
187
298
73
166
996
883
471
179
343
336
206
186
43
M
M
F
F
M
M
F
M
F
piss
bitch
dang/darn
shoot
crap
gay
heck
idiot
jesus
golly
Lord
jerk
mother fucker
asshole
nigger
114
71
96
91
53
50
69
37
31
38
30
42
7
10
5
98
73
62
43
83
73
42
47
42
30
23
18
30
32
27
F
M
F
F
M
M
F
M
M
F
F
F
M
M
M
Cat
A
B
C
D
E
F
G
I
L
M
N
O
P
T
X
F
25
71
18
2
223
299
2146
230
117
79
23
M
43
98
82
4
541
535
1608
356
185
99
26
UK Data
M
M
M
M
M
F
F
M
F
M
F
151
274
17
6
610
352
21
25
F
M
F
F
ab
G
E
P
N
F
I
L
A
B
D
C
M
O
T
R
c1
G
E
N
P
I
F
B
A
L
M
C
T
D
O
R
c2
E
G
N
P
I
F
B
C
D
M
L
T
A
O
R
de
E
G
N
P
I
B
F
D
M
C
L
T
A
O
R
Cat
G
F
E
O
P
I
L
M
B
C
A
N
T
X
D
Total
3754
834
764
761
626
586
302
178
169
100
68
49
38
31
6
Target below, Male Female
speaker across
Male
93
25
Female
37
23
Conclusion
• Work on-going – the exploitation of the US data is
far from being complete. New UK dataset
available.
• Similar patterns because of a shared cultural
heritage?
• Corpus data can, and has been, of use in the
study of swearing. It is of particular use in looking
at differences in usage through a range of
variables
• It is certainly an area where the corpus and other
methodologies can combine
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Putting it Crudely