A Study on the Attitudes of Chinese and Foreign Media toward DeepSeek

Main Article Content

Zihan Liu https://orcid.org/0009-0000-7269-5444

Keywords

A Study on the Attitudes of Chinese and Foreign Media toward DeepSeek

Abstract

In recent years, large - scale language models (LLMs), such as ChatGPT, Claude, and DeepSeek, have rapidly developed, sparking global discussions on technology and ethics. DeepSeek, as an open - source large - scale model developed by the Chinese team DeepSeek, has shown outstanding technical performance and application potential, attracting widespread attention from domestic and foreign media. However, there are significant differences in the reporting framework of DeepSeek among media from different cultural backgrounds, political stances, and technological ecosystems. The article adopts a sample selection method, and the media scope includes the following: Chinese media: Science and Technology Daily; Asian media: The Straits Times (Singapore); and European and American media: The New York Times. Time range: June 2023 to March 2025. Focusing on China’s local AI representative ‘DeepSeek’, filling the gap in current LLM media research on ‘Chinese model’ cases. By combining framing theory with critical discourse analysis (CDA), media coverage in the Chinese and American regions can be compared. Mixed content analysis (quantitative) and discourse analysis (qualitative) reveal explicit and implicit attitude structures.

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References

  • Chen, X. and Wang, Z. (2025). Discourse analysis of artificial intelligence issues in foreign media from the perspective of appraisal theory attitude. English Square, no. 13, pp. 59-63.
  • Xiong, P. and Shan, Y. (2025). The mechanism, scenarios and implications of DeepSeek driving the development of new quality productive forces of enterprises. Enterprise Research, no. 4, pp. 8-12.
  • Zhong, X. and Zhao, N. (2025). The logical dimensions, internal mechanism and promotion path of DeepSeek in facilitating digital civilization construction. Journal of Changsha University of Science and Technology (Social Science Edition), vol. 40, no. 5, pp. 65-75.

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