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China's Image in Sino-US Trade War Reporting from the Perspective of Critical Discourse Analysis: Comparative Study on the Report of China Daily in 2018 and 2025
Abstract
This study explores how China Daily, China’s official English-language newspaper, constructs and reconstructs China’s national image across two critical phases of the Sino–US trade war—2018 and 2025. Drawing on Fairclough’s Three-Dimensional Model of Critical Discourse Analysis (CDA) and Halliday’s Systemic Functional Grammar (SFG), the research adopts a mixed-method approach combining quantitative corpus analysis and qualitative discourse interpretation. The two self-built corpora, each comprising 50 trade-related reports from the respective years, are analyzed in terms of transitivity, modality, reporting modes, and news sourcing. The findings reveal significant diachronic shifts in China Daily’s discourse strategies. In 2018, the newspaper’s coverage emphasized defensive and cooperative tones, with frequent use of low-value modality and indirect reporting to project a cautious yet resilient image. By contrast, in 2025, China Daily employed more assertive linguistic features, including increased use of high-value modals, a higher proportion of direct quotations, and a predominance of specific, identifiable news sources. These shifts signal a transformation from a passive, defensive posture to a confident, proactive representation of China as a responsible global power advocating fairness, stability, and multilateral cooperation. Theoretically, this research enriches CDA studies by providing a diachronic perspective on national image construction within domestic English-language media. Practically, it contributes to understanding how strategic discourse serves as a tool of international communication and ideological negotiation in the evolving landscape of China–US relations.
Article information
Journal
International Journal of Linguistics, Literature and Translation
Volume (Issue)
8 (11)
Pages
49-68
Published
Copyright
Copyright (c) 2025 Haobo Sun
Open access

This work is licensed under a Creative Commons Attribution 4.0 International License.

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