Artificial Intelligence in Climate Change Journalism: Assessing Perceived News Credibility, Readability, and Influence on Climate Change Mitigation Intention
Yu Guo, Siyu Xu, Xiaodong Yang
🤖 gxceed AI 要約
日本語
AI生成ニュースと人間作成ニュースの気候変動報道における信頼性、可読性、緩和意図への影響を実験で検証。人間作成ニュースは肯定的不一致を生むが、AI生成ニュースは否定的不一致を示す。ナラティブ形式が緩和意図を促進し、AIニュースのアルゴリズム改善により可読性の不一致が緩和意図を高める可能性が示唆された。
English
This study experimentally compares AI-generated vs. human-generated climate change news on credibility, readability, and mitigation intentions. Human news yielded positive disconfirmation, while AI news led to negative disconfirmation. Narrative style significantly promoted mitigation intentions, and improving algorithmic narrative structure in AI content could enhance readability expectations and engagement.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文は気候変動ジャーナリズムにおけるAI活用の効果を検証し、日本のメディアや企業の情報発信に示唆を与えるが、直接的なGX政策や開示規制との関連は薄い。
In the global GX context
The paper contributes to global climate communication strategies by showing how AI-generated news can be optimized to enhance public engagement, relevant for media organizations and climate communicators worldwide.
👥 読者別の含意
🔬研究者:Provides experimental evidence on AI vs. human news effects in climate communication, useful for scholars in climate journalism and AI-mediated persuasion.
🏢実務担当者:News organizations can apply the findings to design AI-generated climate content with narrative styles that foster mitigation intentions.
📄 Abstract（原文）
As artificial intelligence-generated (AI-generated) journalism rises, its effectiveness in engaging the public to mitigate climate change remains uncertain. This study integrates the MAIN model and expectation-confirmation theory to examine how AI-generated versus human-generated climate change news influences audience news evaluation and behavioral intentions. We conducted a 2 (human-generated news vs. AI-generated news) × 2 (authorship disclosed vs. authorship undisclosed) × 2 (narrative vs. non-narrative) between factorial experiment (N = 441) to test the effects of news source, authorship disclosure, and narrative style on readers’ perceived news credibility, readability, and behavioral intentions for climate change mitigation. The t-tests revealed that human-generated climate change news elicited positive disconfirmation, while AI-generated news resulted in negative disconfirmation. A three-way MANCOVA revealed that narrative news significantly promoted climate change mitigation intentions. Further moderation analyses indicated that while AI-generated news did not differ significantly from human-generated news in terms of persuasiveness, improving the algorithmic narrative structure of AI-generated content could enhance positive disconfirmation of readability, thereby increasing the likelihood of engagement in mitigation behaviors. These findings provide insights into optimizing AI-generated journalism to enhance climate change communication and public engagement.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/1461670x.2026.2686737first seen 2026-06-18 05:50:39
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