Yerin Kim, Byungjun Kim, Min Hyung Park, Woomin Nam, Jangil Kim
{"title":"A Soft Power Challenge, or an Opportunity? A Big Data Analysis on Chinese Soft Power during COVID-19 Pandemic","authors":"Yerin Kim, Byungjun Kim, Min Hyung Park, Woomin Nam, Jangil Kim","doi":"10.1093/fpa/orad011","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":46954,"journal":{"name":"Foreign Policy Analysis","volume":" ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foreign Policy Analysis","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1093/fpa/orad011","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INTERNATIONAL RELATIONS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Reflecting the diverse, comparative and multidisciplinary nature of the field, Foreign Policy Analysis provides an open forum for research publication that enhances the communication of concepts and ideas across theoretical, methodological, geographical and disciplinary boundaries. By emphasizing accessibility of content for scholars of all perspectives and approaches in the editorial and review process, Foreign Policy Analysis serves as a source for efforts at theoretical and methodological integration and deepening the conceptual debates throughout this rich and complex academic research tradition. Foreign policy analysis, as a field of study, is characterized by its actor-specific focus. The underlying, often implicit argument is that the source of international politics and change in international politics is human beings, acting individually or in groups. In the simplest terms, foreign policy analysis is the study of the process, effects, causes or outputs of foreign policy decision-making in either a comparative or case-specific manner.