The Covid-19 pandemic in Greece through the lens of methodological synergy: Combining corpus linguistics and critical discourse analysis approaches

Dimitris Elafropoulos
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Abstract

Computational approaches have been increasingly used in social sciences in recent years. The present study investigates to what extent a triangulated framework of corpus linguistics and critical discourse analysis can shed light on cutting-edge approaches and offer new insights into social research. Big data sets that may be examined computationally reveal patterns, trends and co-occurrences of elements (Teubert & Krishnamurthy, 2007, p. 6). This research is based on analysing a 720-text corpus of political and scientific discourse (March 2020 - May 2022) of representatives of three main institutions (government, main opposition party, and Greek public health organisation). Mediated political and scientific discourse has been the primary source of information regarding the Covid-19 pandemic crisis. The computational techniques focus on the representation of the pandemic and the relevant collocations and concordances in an attempt to navigate afterwards through qualitative analysis. Findings indicate that the government constructed a ‘‘rescue narrative’’ while the main opposition party advocated working-class priorities. Conceptual metaphors regarding the pandemic were pervasive both in political and scientific discourse. Comparative studies among different countries could be conducted in the future.
从方法论协同作用的角度看希腊的 Covid-19 大流行病:结合语料库语言学和批判性话语分析方法
近年来,计算方法越来越多地应用于社会科学领域。本研究探讨了语料库语言学和批判性话语分析的三角框架能在多大程度上揭示前沿方法并为社会研究提供新见解。可通过计算检查的大数据集揭示了各种要素的模式、趋势和共现(Teubert & Krishnamurthy, 2007, p.6)。本研究基于对三个主要机构(政府、主要反对党和希腊公共卫生组织)代表的 720 篇政治和科学话语语料(2020 年 3 月至 2022 年 5 月)的分析。通过媒介传播的政治和科学话语是有关 Covid-19 大流行病危机的主要信息来源。计算技术的重点是大流行病的表述以及相关的搭配和连词,试图通过定性分析为之后的分析提供导航。研究结果表明,政府构建了 "救援叙事",而主要反对党则主张工人阶级优先。有关大流行病的概念隐喻在政治和科学话语中都很普遍。今后可对不同国家进行比较研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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