ArticleHeliyon2022
Media framing and expression of anti-China sentiment in COVID-19-related news discourse: An analysis using deep learning methods.
Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A bimodal longitudinal investigation on changes in sentiments over social media interactions owing to COVID-19 pandemic.Scientific reports · 2025Article
- Media framing in the digital age: Interplay of real estate and welfare narratives in South Korean news articles.Heliyon · 2024Article
- Stigmatizing Monkeypox and COVID-19: A Comparative Framing Study ofInternational journal of environmental research and public health · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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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.
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