Discursive use of stability in New York Times’ coverage of China: a sentiment analysis approach

Guofeng Wang (), Yilin Liu and Shengmeng Tu
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Guofeng Wang: Shanghai Normal University
Yilin Liu: Shanghai Normal University
Shengmeng Tu: Shanghai Normal University

Palgrave Communications, 2023, vol. 10, issue 1, 1-10

Abstract: Abstract The importance of stability has been consistently emphasized in China and the discursive use of stability is found to have legitimizing effects in Chinese newspapers, but how such political keywords are employed by the newspaper of a country that is ideologically distinct from China remains underexplored. This study addresses this gap by investigating the use of stability in The New York Times’ coverage of China between 1980 and 2020, drawing on critical discourse analysis (particularly, the discourse-historical approach) and sentiment analysis. A diachronic quantitative analysis demonstrates an overall negative sentiment in news reports relating to China’s stability across these years, with positive sentiment evident only during the 1980s and negative sentiment prevailing from 1990 to 2020. These findings are consistent with general trends in US-China relations and US foreign policy over the four decades. Qualitative analysis reveals that negative sentiment focuses on sociopolitical and territorial issues, whereas positive sentiment focuses primarily on economic and financial aspects, indicating that the newspaper views the issue of China’s stability from a politically self-interested perspective of the US and is also concerned about the persistence of certain dominant ideologies in American society. This study contributes to a greater comprehension of the use of political keywords in national and international news discourse, especially by the media of ideologically diverse societies. Moreover, because the application of sentiment analysis to critical discourse analysis and news discourse analysis has proven to be time-efficient, verifiable, and accurate, researchers can confidently employ it to disclose hidden meanings in texts.

Date: 2023
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DOI: 10.1057/s41599-023-02165-0

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