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Gender, Sexuality and Bad Language Tony McEnery, Department of Linguistics and Modern English Language, University of Lancaster.

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Presentation on theme: "Gender, Sexuality and Bad Language Tony McEnery, Department of Linguistics and Modern English Language, University of Lancaster."— Presentation transcript:

1 Gender, Sexuality and Bad Language Tony McEnery, Department of Linguistics and Modern English Language, University of Lancaster

2 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.

3 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 Womans Place (Lakoff, 1975) – Swearing (Hughes, 1991, 1998)

4 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 …..

5 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

6 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

7 Further annotation is added to reveal


9 There is plenty to do! u Lets quickly look at some claims made by Hughes, then move on to look at gay, queer, puff and fuck. First Hughes: u Claim one - the categories of swearing. Which words fall into which categories u Claim two - which words are used to insult which sex




13 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

14 Gays, queers and poofs Data is sparse But even on a small scale the data is interesting Gay (24 examples) Collocates: Is(10), Hes (9), Youre (2), Dads (1), Whos (1) A prosody of attribution in nearly all of the cases (21)

15 Strong colligation with the X is gay pattern. The X is male: Hes (9), he (3), chap (1), dads (1), Mick (1), James (1), Male (1), Pat (1), Phil (1), sons (1) Interestingly, no personal attributions of being gay.

16 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?

17 Poofter (6 examples) & poof (2 examples) Singular common nouns. Always P abuse. Not used in an attributive manner


19 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

20 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 lets look at fuck

21 Male v. Female - word forms 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.

22 Full Categories

23 Categories typeMFTendency A5352M B202115M C5753M D7045M E M F190200F G F I234225M L5890F M6944M N413517F O1920F P403359M R913F T1935F

24 Swearing in reported speech Why is the relative proportion of reported uses of fuck higher for females (roughly one sixth of examples as opposed to one thirty fifth?)

25 Targets 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.

26 Keywords Notice we have evidence for a tentative explanation of the reported speech discrepancy

27 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

28 female3681 male4585 unknown3399


30 WordFM fuckage12 fucked1786 fucker831 fuckers212 Fuckery3 fuckhead1 fuckheads fuckin940 Fucking fucks12

31 lemmafemalemale shit379996M fuck255883M god795471F gosh404179F damn179343M hell187336M gee/geez298206F ass73186M goodgrief/goodness/gracious/heavens16643F piss11498F bitch7173M dang/darn9662F shoot9143F crap5383M gay5073M heck6942F idiot3747M jesus3142M golly3830F Lord3023F jerk4218F mother fucker730M asshole1032M nigger527M

32 CatFMUK Data A2543M B7198M C1882M D24M E223541M F299535F G F I230356M L117185F M7999M N2326 F O151610F P274352M T1721F X625F

33 abc1c2de GGEE EEGG PNNN NPPP FIII IFFB LBBF AACD BLDM DMMC CCLL MTTT ODAA TOOO RRRR 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


35 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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