Media framing and expression of anti-China sentiment in COVID-19-related news discourse: An analysis using deep learning methods

Abstract

This study focuses on news content related to China and COVID-19 during the COVID-19 pandemic and investigates how media frame, affected the emergence of anti-China sentiments through a case study of Japanese online news discourse. We collected large-scale digital trace data including online news and comments during the COVID-19 pandemic. By employing deep learning-based sentiment classifications, we were able to measure the extent of anti-China sentiments expressed through comments during the pandemic's different phases and on different types of news content. Our results provide empirical evidence that the news media's negative depictions of China and coverage related to political and international relations issues increased as the prevalence of COVID-19 in Japan increased. Importantly, since this coverage can prompt the expression of anti-China sentiment, we argue that the framing used by the media can provide discursive contexts that escalate COVID-19 issues into a broader expression of anti-China sentiment. This study not only identifies the impact of media frames on the expression of anti-China sentiment but also contributes to the development of methods for detecting public opinion and measuring the framing effect with big data and advanced computational tools.