title: Corpus-based discourse analysis of the lexemes related to COVID-19 pandemic in the German magazine Der Spiegel (online version)
metadata.name: dc.contributor.author
metadata.value: Jesan, Irina J.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kovtunova, Elena A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Grigorieva, Liubov N.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-03-24T10:40:03Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-03-24T10:40:03Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2022-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Jesan I. J., Kovtunova E. A., Grigorieva L. N. Corpus-based discourse analysis of the lexemes related to COVID-19 pandemic in the German magazine Der Spiegel (online version). Vestnik of Saint Petersburg University. Language and Literature. 2022, 19 (4): 760–779. https://doi.org/10.21638/spbu09.2022.407 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu09.2022.407
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/39408
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The article gives a detailed insight into pandemic media discourse by revealing and analyzing
  its lexical changes. The research aims to observe the dynamic processes in the vocabulary:
  the intensity and usage frequency of the key discourse words, neologisms, borrowings, the
  reflection processes of important social and political events in the vocabulary. The research is
  based on the DIMEAN multilevel linguistic and discourse analysis model, elements of corpus
  analysis, concordance compilation with AntConc program and Voyant Tools web application,
  as well as methods of dictionary definitions and contextual semantic analysis. The material of
  the study is based on the current theme-based publications from the German news website
  Der Spiegel dated 2020 and 2022. Structuring the material into two sub-corps (according to
  the year, 50 articles each) enables to demonstrate a linguistic analysis of a specific time period
  and present our observations on lexical changes of German COVID-19 pandemic media discourse.
  The study applies both traditional and modern methods of computer analysis of modern
  media discourse that is relevant to the current linguistic study approach. The new multilevel
  comparative analysis of the theme-based media discourse by means of corpus analysis
  allows to track lexical changes and to observe the dynamics and evaluation of topics in the
  socio-political discourse. As a result of conducted research in two sub-corps were revealed
  the most frequent lexemes taking into account macro- and micro-contexts; in the group of the
  keywords most relevant for the two periods the contextual-semantic analysis was provided.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Language and Literature;Volume 19; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: media discourse
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: DIMEAN
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: corpus analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: corona vocabulary
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Der Spiegel
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Corpus-based discourse analysis of the lexemes related to COVID-19 pandemic in the German magazine Der Spiegel (online version)
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 07.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/39408/1/07.pdf
resources.format: Adobe PDF
resources.size: 992,56 kB