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 non-corpus 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 revealField Feature Possible values1 Gender (sp) M = male, F= female X = unknown2 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 following5 Gender (he) As per gender of speaker6 Person (target) 1 = first person, 2 = second person, 3 =

third person, X = unknown7 Metalinguistic 0 = no, 1 = yes8 Animacy

(target)+ = animate, - = non-animate, X =unknown

9 Gender (target) As per gender of speaker10 Number (target) 1 = singular, 2 = plural, X = unknown11 Quotation Q = quotation, N = non-quotation, X =

unknown

Category Label Description ExamplePersonal P A second person

insult of the form"You X" orsimilar

Yeah but Jonesyain't here youcunt so it's onlyme.

Personal byreference

R A third personinsult of the form"The X" orsimilar

fucking servesthe cunt right aswell

Destinational D Swear wordfollowed typicallyby off

Oh Jake sod off.

Cursing C An insult of theform "X you" orsimilar.

Can't even comeand say hello, sobugger him!

Generalexpletive ofanger,frustration orannoyance

G An imprecationwith no particulartarget.

Oh shit , Ihaven't boughtany scissors!

Explicitexpletive ofanger,frustration orannoyance

E An imprecationwith a specifictarget of the form"X it!" or similar

Liz got quitecross you knowshe's quite ohbugger 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 R D C G Ecunt * * 0 0 0 0shit * * 0 0 * 0fart * * 0 0 0 0bugger * * * * * *bastard * * 0 0 0 0arsehole * * 0 0 0 0

Term Category (Our data)P R D C G E

cunt * * 0 0 0 0shit * * 0 0 * 0fart * * 0 0 0 0bugger * * * * * *bastard * * 0 0 + 0arsehole 0 * 0 0 + 0

Male targets only Prick, cunt, twat, pillock, tit,arsehole, shit, turd fart, idiot,imbecile, moron, cretin, prat, swine,pig

Female targets only Cow, bitch, sow, fucker

Male targets only Prick, cunt, pillock, shit, moron,Female targets onlyTargets may be of either sex Twat, arsehole, fart, idiot, prat, cow,

bitch, swine, pig, bastard, bugger,sod

No example of the word found aspersonal insult in the LCA

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 Negative prosody

Attribution Nominal ?

gay

Attribution Abuse/Swearing

Nominalgay ???

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 Male FemaleRoot 197 76-ing 982 226-ed 18 12-er 8 2-s 3 2-ers 2 0Total 1210 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 CategoriesCode Description

A Predicative negative adjective: “the film is shit”

B Adverbial booster: “Fucking marvellous” “Fucking awful”

C Cursing Expletive: “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 Idiomatic ‘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 ‘Pronominal’ form with undefined referent: “got shit to do”

PPersonal 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

Categoriestype M F Tendency

A 53 52 MB 202 115 MC 57 53 MD 70 45 ME 1131 822 MF 190 200 FG 799 1250 FI 234 225 ML 58 90 FM 69 44 MN 413 517 FO 19 20 FP 403 359 MR 9 13 FT 19 35 F

Swearing in reported speech

Quotation Male Female Yes 118 219 No 3790 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, speaker across

Male Female

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.

KeywordsGender Keyword Negative

keywordMale fucking Said, sheFemale Said, she,

her,because,bloodycigarette

Of, its, theold, a, he’s,as, they,first

•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 3681male 4585unknown 3399

Word F Mfuckage 1 2fucked 17 86fucker 8 31fuckers 2 12Fuckery 3fuckhead 1fuckheadsfuckin 9 40Fucking 168 444fucks 1 2

lemma female maleshit 379 996 Mfuck 255 883 Mgod 795 471 Fgosh 404 179 Fdamn 179 343 Mhell 187 336 Mgee/geez 298 206 Fass 73 186 Mgoodgrief/goodness/gracious/heavens 166 43 F

piss 114 98 Fbitch 71 73 Mdang/darn 96 62 Fshoot 91 43 Fcrap 53 83 Mgay 50 73 Mheck 69 42 Fidiot 37 47 Mjesus 31 42 Mgolly 38 30 FLord 30 23 Fjerk 42 18 Fmother fucker 7 30 Masshole 10 32 Mnigger 5 27 M

Cat F M UK DataA 25 43 MB 71 98 MC 18 82 MD 2 4 ME 223 541 MF 299 535 FG 2146 1608 FI 230 356 ML 117 185 FM 79 99 MN 23 26 F

O 151 610 FP 274 352 MT 17 21 FX 6 25 F

ab c1 c2 deG G E EE E G GP N N NN P P PF I I II F F BL B B FA A C DB L D MD M M CC C L LM T T TO D A AT O O OR R R R

Cat Total

G 3754

F 834

E 764

O 761

P 626

I 586

L 302

M 178

B 169

C 100

A 68

N 49

T 38

X 31

D 6

Target below, speaker across

Male Female

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