Social Differentiation in the Use of English Vocabulary

Abstract

In this article, we undertake selective quantitative analyses of the demographi-cally-sampled spoken English component of the British National Corpus (for brevity, referred to here as the ''Conversational Corpus"). This is a subcorpus of c. 4.5 million words, in which speakers and respondents (see I below) are identified by such factors as gender, age, social group, and geographical region. Using a corpus analysis tool developed at Lancaster, we undertake a comparison of the vocabulary of speakers, highlighting those differences which are marked by a very high X2 value of difference between different sectors of the corpus according to gender, age, and social group. A fourth variable, that of geographical region of the United Kingdom, is not investigated in this article, although it remains a promising subject for future research. (As background we also briefly examine differences between spoken and written material in the British National Corpus [BNC].) This study is illustrative of the potentiality of the Conversational Corpus for future corpus-based research on social differentiation in the use of language. There are evident limitations, including (a) the reliance on vocabulary frequency lists and (b) the simplicity of the transcription system employed for the spoken part of the BNC The conclusion of the article considers future advances in the research paradigm illustrated here.

Introducing a corpus of conversational stories. Construction and annotation of the Narrative Corpus

“…Rayson et al (1997) found the forms said and says strikingly high up among the most frequently used words in social classes C2 and DE. In the NC, both said and says are overwhelmingly used as quotatives introducing direct speech presentation.…”

“…The demographically-sampled subcorpus (henceforth BNC-C) consists of roughly 4.5 million words and is often referred to as the 'conversational' subcorpus because the transcripts assembled in this subcorpus "consist of casual conversations" (Aston & Burnard 1998: 28;Hoffmann et al 2008: 32-39; see also Rayson et al 1997 andBiber et al 1999: 133).…”

“…First, since stories may include utterances that are longer than utterances in turn-by-turn talk we focussed on utterances of more than 15 words length, that is, a third more than average utterance length (cf. Rayson et al 1997). Second, we looked for stretches of conversational text in which one speaker occupied every 'third' slot.…”

Analyzing Word Frequencies in Large Text Corpora Using Inter-arrival Times and Bootstrapping

“…Linguists have gone as far as claiming that hypothesis testing of word frequencies is rarely useful to finding associations, and often leads to misleading results [14]. Others have noted that a measure of dispersion is necessary to improve significance testing [21], or that each significant result should be checked using an effect size measure [9] or manual investigation [20].…”

Supporting Law Enforcement in Digital Communities through Natural Language Analysis

“…Keyword profiling [22], exploiting comparative word frequencies, has been used in the past to investigate the differences between spoken and written language [23], British and American English [24], and language change over time. Rayson [25] extended the keywords methodology to extract key grammatical categories and key domain concepts using tagged data in order to make it scalable.…”