Decoding AI discourse: contrastive analysis of media representations in German and Chinese contexts (2018–23) using machine learning techniques

Abstract Previous studies have primarily focused on artificial intelligence (AI) discourse within specific language media, with limited contrastive analyses across different cultural contexts. This study analyzes the representation of AI in German and Chinese media discourses from 2018 to 2023, employing a modified version of Ruth Wodak’s discourse analysis framework alongside advanced machine learning methods. Our findings indicate that both German and Chinese media concentrate on AI issues pertinent to their regions. Chinese media adopt a perspective strategy by frequently quoting political figures, particularly President Xi Jinping, and consistently maintain a positive stance on AI. Conversely, German media, especially after the launch of ChatGPT, highlight high-tech figures and adopt a more critical and cautious approach toward AI. These differences in media discourses arise from distinct media cultural systems shaped by their respective contexts. In China, media outlets are party-affiliated and promote a narrative framing AI as a national strategic endeavor crucial for economic growth, reflecting governmental viewpoints. In contrast, media from Germany, Austria, and Switzerland present diverse perspectives on AI, expressing significant concerns about its potential risks. This study offers valuable insights for interpreting and formulating AI policies across different nations.

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Publication Details

Journal
Digital Scholarship in the Humanities
Published
2025-12-06
DOI
https://doi.org/10.1093/llc/fqaf143
Citations
1
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
5.34
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article

Decoding AI discourse: contrastive analysis of media representations in German and Chinese contexts (2018–23) using machine learning techniques

Kuanyong QIU, Timo Hackel
1 citations
Digital Scholarship in the Humanities
Computational and Text Analysis Methods
5.34
article

Decoding AI discourse: contrastive analysis of media representations in German and Chinese contexts (2018–23) using machine learning techniques

Kuanyong QIU, Timo Hackel
article en
1 citations

Abstract

Abstract Previous studies have primarily focused on artificial intelligence (AI) discourse within specific language media, with limited contrastive analyses across different cultural contexts. This study analyzes the representation of AI in German and Chinese media discourses from 2018 to 2023, employing a modified version of Ruth Wodak’s discourse analysis framework alongside advanced machine learning methods. Our findings indicate that both German and Chinese media concentrate on AI issues pertinent to their regions. Chinese media adopt a perspective strategy by frequently quoting political figures, particularly President Xi Jinping, and consistently maintain a positive stance on AI. Conversely, German media, especially after the launch of ChatGPT, highlight high-tech figures and adopt a more critical and cautious approach toward AI. These differences in media discourses arise from distinct media cultural systems shaped by their respective contexts. In China, media outlets are party-affiliated and promote a narrative framing AI as a national strategic endeavor crucial for economic growth, reflecting governmental viewpoints. In contrast, media from Germany, Austria, and Switzerland present diverse perspectives on AI, expressing significant concerns about its potential risks. This study offers valuable insights for interpreting and formulating AI policies across different nations.

Digital Scholarship in the Humanities
Shanghai Jiao Tong University (CN), University of Zurich (CH), Zurich University of Teacher Education (CH), Oldham Council (GB)
Openalex Percentile: Top 3%
Computational and Text Analysis Methods
5.34
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Decoding AI discourse: contrastive analysis of media representations in German and Chinese contexts (2018–23) using machine learning techniques — Kuanyong QIU, Timo Hackel · Digital Scholarship in the Humanities (2025) | TGRS Research Map | TGRS