Corpus-Based Vocabulary Analysis of English Podcasts

Autor:	Shoaziz Sharakhimov, Ulugbek Nurmukhamedov
Rok vydání:	2021
Předmět:	050101 languages & linguistics Linguistics and Language Vocabulary media_common.quotation_subject 05 social sciences 050301 education Language and Linguistics Linguistics Education Public discourse Corpus based 0501 psychology and cognitive sciences Sociology 0503 education media_common
Zdroj:	RELC Journal. 54:7-21
ISSN:	1745-526X 0033-6882
DOI:	10.1177/0033688220979315
Popis:	In addition to movies, television programs, and TED Talks presentations, podcasts are an increasingly popular form of media that promotes authentic public discourse for diverse audiences, including university professors and students. However, English language teachers in the English as a second language/English as a foreign language contexts might wonder: “How do I know that my students can handle the vocabulary demands of podcasts?” To answer that question, we have analyzed a 1,137,163-word corpus comprising transcripts from 170 podcast episodes derived from the following popular podcasts: Freakonomics; Fresh Air; Invisibilia; Hidden Brain; How I Built This; Radiolab; TED Radio Hour; This American Life; and Today Explained. The results showed that knowledge about the most frequent 3000 word families plus proper nouns (PN), marginal words (MW), transparent compounds (TC), and acronyms (AC) provided 96.75% coverage, and knowledge about the most frequent 5000 word families, including PN, MW, TC, and AC provided 98.26% coverage. The analysis also showed that there is some variation in coverage among podcast types. The pedagogical implications for teaching and learning vocabulary via podcasts are discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7e7f59d812d988d82ae95f17fdf1d736 https://doi.org/10.1177/0033688220979315 Zobrazit plný text záznamu