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Fake News Has a New Author, Can You Spot the Lie? Media Cues and the Detection of AI-Generated Health News Through the Lens of the Media Evocation Paradigm

Zehang Xie & Benjamin (Benjy) J. Li

Health Communication2026https://doi.org/10.1080/10410236.2026.2621231article
ABDC B
Weight
0.37

Abstract

As generative artificial intelligence (GAI) increasingly contributes to the creation of news content, its ability to produce authoritative yet fabricated information raises pressing concerns for public trust and misinformation detection. Guided by the media evocation paradigm (MEP), this study examines how source credibility, content style, and communication channel influence users' ability to detect GAI-generated health fake news. A 2 × 2 × 3 mixed experimental design (N = 120) was employed, in which participants evaluated nine GAI-generated news items across television, newspapers, and social media. Results show that non-authoritative sources, rational framing, and social media platforms significantly enhanced detection accuracy. In contrast, authoritative sources and emotional content in traditional media environments reduced detection rates. A significant three-way interaction reveals that detection accuracy was highest when all three media cues aligned (non-authoritative source, rational style, and social media context). This study extends the MEP to the context of GAI-generated health news and highlights the importance of reflective media processing in how individuals assess information credibility. By identifying how specific combinations of media cues affect fake news detection, the findings offer practical implications for improving public resilience against health misinformation and inform the design of more effective communication strategies in GAI-mediated health contexts.

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https://doi.org/https://doi.org/10.1080/10410236.2026.2621231

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@article{zehang2026,
  title        = {{Fake News Has a New Author, Can You Spot the Lie? Media Cues and the Detection of AI-Generated Health News Through the Lens of the Media Evocation Paradigm}},
  author       = {Zehang Xie & Benjamin (Benjy) J. Li},
  journal      = {Health Communication},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/10410236.2026.2621231},
}

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Fake News Has a New Author, Can You Spot the Lie? Media Cues and the Detection of AI-Generated Health News Through the Lens of the Media Evocation Paradigm

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.