Mixed Messages: Computational Approaches to Cross-Corpus Comparison of Chinese Media

Abstract

The Chinese government strategically leverages its state-controlled media to shape both domestic and international perspectives on key issues. However, there exists limited literature comparing China's domestic media objectives with its international media objectives. First, this thesis introduces the media objective elicitation model, in which government media objectives can be uncovered by contrasting domestic and international messaging. Computational methods of cross-corpus comparison are then applied to a novel corpus of nearly 1 million articles written in the home and overseas edition of the China Daily between 2020 and 2024, a broadly-circulated newspaper controlled by the Chinese Communist Party. First, BERTopic and cosine-based topic alignment are used to discover differences in the types of content included. Then, aspect-collocate and aspect-based sentiment analysis are used to characterize differences in how certain topics are framed. Overall, this thesis discovers evidence that (1) bribery convictions of high-level officials are excluded from the home edition, (2) the overseas edition serves as a shield by responding to Western criticism while omitting Western viewpoints in the home edition, and (3) the home edition portrays Western and Taiwanese politicians far more negatively than the overseas edition. In addition, this thesis illustrates the effectiveness of a computational approach to cross-corpus comparison of news media.