Swearing in informal spoken English: 1990s – 2010s

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates changes in swearing usage in informal speech using large-scale corpus data, comparing the occurrence and social distribution of swear words in two corpora of informal spoken British English: the demographically-sampled part of the Spoken British National Corpus 1994 (BNC1994) and the Spoken British National Corpus 2014 (BNC2014); the compilation of the latter has facilitated large-scale, diachronic analyses of authentic spoken data on a scale which has, until now, not been possible. A form and frequency analysis of a set of 16 ‘pure’ swear word lemma forms is presented. The findings reveal that swearing occurrence is significantly lower in the Spoken BNC2014 but still within a comparable range to previous studies. Furthermore, FUCK is found to overtake BLOODY as the most popular swear word lemma. Finally, the social distribution of swearing across gender and age groups generally supports the findings of previous research: males still swear more than females, and swearing still peaks in the twenties and declines thereafter. However, the distribution of swearing according to socio-economic status is found to be more complex than expected in the 2010s and requires further investigation. This paper also reflects on some of the methodological challenges associated with making comparisons between the two corpora.
Original languageEnglish
Pages (from-to)739-762
Number of pages24
JournalText and talk
Volume41
Issue number5-6
Early online date16 Aug 2021
DOIs
Publication statusPublished - 1 Oct 2021

Bibliographical note

© 2021 Robbie Love, published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords

  • bad language
  • corpus linguistics
  • language change
  • spoken English
  • spoken corpora
  • swearing
  • taboo language

Fingerprint

Dive into the research topics of 'Swearing in informal spoken English: 1990s – 2010s'. Together they form a unique fingerprint.

Cite this