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The Framing in Media Policy Narratives of Artificial Intelligence Generated Content: A Comparative Between Traditional Media and Social Media in China

Author

Listed:
  • Jie Liu
  • Wenjing Dong
  • Xinyu Wang
  • Gongjing Gao

Abstract

Artificial intelligence generated content (AIGC) technology has faced both acclaim and controversy since its inception. The media narratives and framing drive public discussions and policy debates on new technologies; studying how the media describe and frame AIGC aids in deepening the understanding of how media, as policy actors, engage in debates on emerging technology policies and the intrinsic relationship between media narratives and media frames. By creating a comprehensive analytical framework that incorporates the Narrative Policy Framework and the “beneficial‐risky” media frames, we analyzed the coverage of AIGC by Chinese traditional media (state and local media) and social media creators. The results indicate that, compared to state media, local media and social media exhibit more negative or silent attitudes toward AIGC and publish more narratives portraying villains or victims, whereas state media prefer hero narratives. Media narratives that include heroes are associated with beneficial frames, while villain narratives are linked with more risky frames. Furthermore, the analysis confirmed differences among media outlets, whether acting as information channels or policy debaters, in their use of beneficial/risky frames. This study enriches research on media policy narratives and provides new insights into the nuanced relationship between media narratives and media framing. 人工智能生成内容(AIGC)技术自诞生以来, 既受到赞誉, 也饱受争议。媒体叙事和框架推动着公众对新技术的讨论和政策辩论, 研究媒体如何描述和建构AIGC有助于加深理解“媒体作为政策行动者如何参与新兴技术政策的辩论, 以及媒体叙事与媒体框架之间的内在联系”。通过提出一个包含“叙事政策框架”和“利益‐风险”媒体框架的综合分析框架, 我们分析了中国传统媒体(官方和地方媒体)与社交媒体创作者对AIGC的报道。结果表明, 与官方媒体相比, 地方媒体和社交媒体对AIGC表现出更多负面或沉默的态度¸并发布更多描绘反面角色或受害者的叙事¸而官方媒体则更倾向于正面角色叙事。包含正面角色的媒体叙事与利益框架相关¸而反面角色叙事则与更具风险的框架相关。此外¸分析还证实了不同媒体机构(无论是作为信息渠道还是政策辩论者)在使用利益/风险框架方面存在差异。本研究丰富了媒体政策叙事研究, 并为媒体叙事与媒体框架之间的微妙关系提供了新的见解。 La tecnología de contenido generado por inteligencia artificial (AIGC) ha recibido tanto elogios como controversia desde su creación. Las narrativas y los encuadres mediáticos impulsan las discusiones públicas y los debates políticos sobre nuevas tecnologías. Estudiar cómo los medios describen y encuadran el AIGC ayuda a comprender mejor cómo los medios, como actores políticos, participan en los debates sobre políticas tecnológicas emergentes y la relación intrínseca entre las narrativas y los encuadres mediáticos. Mediante la creación de un marco analítico integral que incorpora el Marco de Políticas Narrativas y los encuadres mediáticos “beneficiosos‐riesgosos”, analizamos la cobertura del AIGC por parte de los medios tradicionales chinos (estatales y locales) y los creadores de redes sociales. Los resultados indican que, en comparación con los medios estatales, los medios locales y las redes sociales muestran actitudes más negativas o silenciosas hacia el AIGC y publican más narrativas que retratan a villanos o víctimas, mientras que los medios estatales prefieren las narrativas de héroes. Las narrativas mediáticas que incluyen héroes se asocian con encuadres beneficiosos, mientras que las narrativas de villanos se vinculan con encuadres más arriesgados. Además, el análisis confirmó las diferencias entre los medios de comunicación, ya sean canales de información o de debate político, en el uso de encuadres beneficiosos/riesgosos. Este estudio enriquece la investigación sobre las narrativas de las políticas mediáticas y proporciona nuevos conocimientos sobre la relación matizada entre las narrativas y el encuadre mediáticos.

Suggested Citation

  • Jie Liu & Wenjing Dong & Xinyu Wang & Gongjing Gao, 2026. "The Framing in Media Policy Narratives of Artificial Intelligence Generated Content: A Comparative Between Traditional Media and Social Media in China," Review of Policy Research, Policy Studies Organization, vol. 43(2), March.
  • Handle: RePEc:bla:revpol:v:43:y:2026:i:2:n:e70030
    DOI: 10.1111/ropr.70030
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