The Representation of the Capital of Kazakhstan in Central Asia Online News Coverage: a Corpus-Assisted Analysis

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Anna Bączkowska
https://orcid.org/0000-0002-0147-2718
Shynar Gabdrakhmanova
Gulnara Akhmetova

Abstrakt

Central Asia (CA) is a region spanning five countries (Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan and Turkmenistan) which emerged after the fall of the Soviet Union. Recently, the biggest CA country - Kazakhstan - spurs a growing interest in the media due to its rapid development and the emerging role as the key player in Central Asia economy, politics and culture. The aim of this paper is to analyse how the capital of Kazakhstan (and the country per se), is represented in online news coverage in a selection of newspapers published in Kyrgyztan, Uzbekistan and Tajikistan. The theoretical and methodological framework of the investigation hinges on the assumptions of the Framing Theory used in media studies on the one hand, and follows a corpus-assisted discourse analysis widely used in linguistic inquiries on the other. The data come from a monitor JSJ corpus, from which virtual corpora illustrating the news published by the above-mentioned countries were teased out. The corpus-based tools used in the quantitative study involve: the analysis of collocations, keywords and frequencies, as well as sentiment analysis. The examination of framing illustrates a qualitative investigation. The results of the study demonstrate that Kazakhstan is represented in online CA news coverage in highly positive terms, as a powerful leader of the whole Central Asia region (in terms of economy, politics and culture).


 

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Jak cytować
Bączkowska, A., Gabdrakhmanova, S. i Akhmetova, G. (2020) „The Representation of the Capital of Kazakhstan in Central Asia Online News Coverage: a Corpus-Assisted Analysis”, ANNALES UNIVERSITATIS PAEDAGOGICAE CRACOVIENSIS. STUDIA LINGUISTICA, (15), s. 16–31. doi: 10.24917/20831765.15.1.
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Artykuły naukowe

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The Representation of the Capital of Kazakhstan in Central Asia Online News Coverage: a Corpus-Assisted Analysis
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