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A Corpus Analysis of Online Education Tweets During Covid-19
Abstract
The current study investigates the writing or register style of 0.2 million tweets on the topic of online education during Covid-19 in July and August 2020. This pandemic delivered a massive jolt not only to the global economy, health, and business, but also to our educational system. According to the most recent UNESCO report, 1.3 billion students worldwide have been unable to attend schools or universities since March 2020. (McCarthy, 2020). As a result, online education has been regarded as grasping at straws. The study intends to highlight frequently used language structure and frequently used lexis from individual users' lexicons via tweets. Furthermore, a deliberately used stock of vocabulary items may be useful in scrutinizing and measuring learners' attitudes toward this pandemic around the world. Style was defined by Biber and Conrad (2009) as "frequent language forms used by speakers" and "an art of effective and forceful communication." As a result, the most frequent words/lemmas were extracted from the entire corpus first, and then a reduced vocabulary with statistical measures was rigorously studied. Furthermore, some other morpho-syntactic linguistic/grammatical features were investigated using the MAT tagger, a statistical computational tool for determining the text type.
Authors
Dr. Urooj Fatima Alvi
Assistant Professor, Department of English Department, University of Education, Lahore, Punjab, Pakistan
Dr. Shafqat Rasool
Professor, Department of Education, Govt. College University Faisalabad, Punjab, Pakistan
Keywords
MAT Tagger, Morpho-Syntactic Linguistic Features, Online Education, Twitter Data, Writing or Register Style
