International Journal of Academic Research in Business and Social Sciences

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A Corpus-Based Analysis of Ministerial Speeches During Natural Crises

Open access

Nor Azikin Mohd Omar, Ku Aimi Arissya Ku Ruslin, Nursalina Ismail, Junaidah Abdul Rahman, Zanirah Wahab

Pages 241-257 Received: 01 Oct, 2024 Revised: 03 Nov, 2024 Published Online: 05 Dec, 2024

http://dx.doi.org/10.46886/IJARBSS/v14-i12/13566
The intersection of disaster management and leadership discourse has garnered growing scholarly interest, particularly in understanding how language shapes the effectiveness of crisis response. In the context of natural disasters, such chaotic situations require strategic communication from leaders who must navigate the complexities of governance under public scrutiny. Taking a corpus-assisted approach to leadership, this study focuses on the linguistic patterns employed by global leaders and ministerial officers in their speeches when addressing natural disasters. The research draws on the Natural Disaster Corpus 2023 (NDC23), comprising 115,970 tokens from 104 official ministerial speeches delivered in response to various natural disasters throughout 2023. The speeches were sourced from open-access platforms, including government websites, United Nations resources, and climate-focused organizations. The corpus was analyzed using AntConc 4.2.4, which facilitated the exploration of word frequency, N-Grams, KWIC, clustering, and collocation patterns in leadership discourse. The analysis highlights the significant role of language in leadership during natural disasters, revealing a reliance on modal verbs, personal pronouns, and present continuous tense as key strategies. These linguistic choices help leaders express intentions, demonstrate ongoing efforts, and establish connections with affected communities as part of their strategies to engage and reassure audiences during crises. The study also provides fresh insights into corpus-assisted methodologies, underscoring the value of macro-level perspectives on language patterns within large datasets, while reinforcing the concept of leadership as a socially rooted practice.
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