Although ongoing research has explored the impact of AI-based messages, studies specifically examining how the interaction between AI sources and message types amplifies this effect remain limited. To address this gap, this study explored the mechanisms through which news sources (human expert vs. AI algorithm) and message framing (narrative vs. numeric) influence public support for climate change policies. Specifically, we investigated the mediating role of news credibility in the relationship between the message source and policy support, and further examined whether news framing moderates this underlying process. Based on a factorial design experiment, we examined the effects of 2 news sources (human expert vs. AI algorithm) and 2 frame types (narrative vs. numeric). The results indicated that the main effect of the news source on policy support was not significant, whereas a significant effect was observed for news credibility. Specifically, perceived news credibility was significantly higher for human sources compared to AI sources. Furthermore, the relationship between the news source and policy support was mediated by news credibility; particularly, human sources indirectly increased policy support by enhancing news credibility. This indirect effect was moderated by news framing, remaining significant under the narrative—but not numerical—condition. Conversely, when AI sources delivered numerical messages, higher news credibility led to greater policy support. This study advances AI-mediated communication literature by proposing effective messaging strategies for human and AI sources in science news.

Authors:
In-Jae Lim, Sungkyunkwan University, South Korea
Heejo Keum, Sungkyunkwan University, South Korea
Jaeho Cho, University of California, United States

About the Presenter(s)
Dr. Hee Jo Keum is a professor in the Department of Media and Communication at Sungkyunkwan University in South Korea

See this presentation on the full schedule – Thursday Schedule

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