Lexical Demands of Political News Reports: A Corpus-Based Lexical Profiling Inquiry

Regular access and exposure to authentic written material is essential for learners of English as a foreign language (EFL) or second language (ESL) beyond the intermediate level, thus necessitating the determination of the vocabulary size required for comprehension of such material. Most research into the effect of text coverage on comprehension has used small corpora, primarily comprising simplified texts. This study demonstrates the word-family sizes necessary for the adequate and optimal comprehension of an accessible genre of authentic text-based material: online political news reports. For this purpose, a corpus of 20 million words was collected from the online New York Times newspaper over a 24-month period. Lexical profiling of the corpus was conducted via Nation’s British National Corpus/Corpus of Contemporary American English (BNC/COCA) 25 word-family lists using the AntWordProfiler software. The monthly corpora reflected relatively consistent text coverage of each of the first 3,000 word-family bands and of the vocabulary sizes necessary to reach the coverage levels widely accepted as prerequisites to achieving adequate (95%) and optimal (98%) comprehension. Results of the whole-corpus analysis indicated that the 95% and 98% text coverage levels were achieved, respectively, within the 3,000–4,000 and 6,000–7,000 word-family bands. These findings render written political news reports in general, and from the New York Times in particular, a useful source of supplementary material for EFL/ESL learners to improve the breadth and depth of their English vocabulary knowledge once they master approximately 3,500 word families.

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Publication Details

Journal
Forum for Linguistic Studies
Published
2025-04-18
DOI
https://doi.org/10.30564/fls.v7i4.8944
Citations
1
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
2.48

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article

Lexical Demands of Political News Reports: A Corpus-Based Lexical Profiling Inquiry

Mohammad Rashid Alfuhaid
1 citations
Forum for Linguistic Studies
Sentiment Analysis and Opinion Mining
2.48
article

Lexical Demands of Political News Reports: A Corpus-Based Lexical Profiling Inquiry

Mohammad Rashid Alfuhaid
article en
1 citations

Abstract

Regular access and exposure to authentic written material is essential for learners of English as a foreign language (EFL) or second language (ESL) beyond the intermediate level, thus necessitating the determination of the vocabulary size required for comprehension of such material. Most research into the effect of text coverage on comprehension has used small corpora, primarily comprising simplified texts. This study demonstrates the word-family sizes necessary for the adequate and optimal comprehension of an accessible genre of authentic text-based material: online political news reports. For this purpose, a corpus of 20 million words was collected from the online New York Times newspaper over a 24-month period. Lexical profiling of the corpus was conducted via Nation’s British National Corpus/Corpus of Contemporary American English (BNC/COCA) 25 word-family lists using the AntWordProfiler software. The monthly corpora reflected relatively consistent text coverage of each of the first 3,000 word-family bands and of the vocabulary sizes necessary to reach the coverage levels widely accepted as prerequisites to achieving adequate (95%) and optimal (98%) comprehension. Results of the whole-corpus analysis indicated that the 95% and 98% text coverage levels were achieved, respectively, within the 3,000–4,000 and 6,000–7,000 word-family bands. These findings render written political news reports in general, and from the New York Times in particular, a useful source of supplementary material for EFL/ESL learners to improve the breadth and depth of their English vocabulary knowledge once they master approximately 3,500 word families.

Forum for Linguistic Studies
Qassim University (SA)
Qassim University
Openalex Percentile: Top 10%
Sentiment Analysis and Opinion Mining
2.48
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