Abstract
Abstract The Chinese government's rigorous efforts to enhance its soft power have confronted a major challenge during the COVID-19 pandemic. This study aimed to look at how the Chinese soft power changed throughout the pandemic using English news articles that covered China. The research took a data science approach to investigate the contents of articles using machine-learning-based sentiment analysis and Dirichlet-Multinomial Regression (DMR) analysis. The results show a gradual downturn in overall sentiment and that the topics related to political issues made the most significant impact. Nevertheless, the major increase in referencing Chinese social media implied that the sources of Chinese soft power have been diversified throughout the pandemic. In addition, this research has aimed to engage in major debates around soft power theory. Providing a multi-disciplinary approach for analyzing soft power, this research has tackled the difficulties in the quantitative conceptualization of soft power.
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