How AI-Assisted Content Creation Affects Self-Media Creator Authenticity and Audience Trust

Naier Zhong

doi:10.6911/

Authors

Naier Zhong University of California, Davis, 95616, USA

DOI:

https://doi.org/10.6911/

Keywords:

AI-assisted content creation, self-media, authenticity, audience trust, generative AI, structural equation modeling, elaboration likelihood model.

Abstract

With the growing popularity of self-media platforms like YouTube, TikTok, and Bilibili, generative AI has been gradually embraced by creators to achieve large-scale content generation, including text, images, and videos. These tools can help increase productivity but can also lead to questions about the authenticity of the content creator and trust by their audience. In this paper, a structural model is developed that combines the Elaboration Likelihood Model and technology-mediated trust theory to explore the causal link between AI disclosure, perceived content quality, perceived authenticity and audience trust. Structural equation modeling is used to analyze a quantitative survey of 427 users of social media. The results show that the perception of authenticity plays a mediating role between disclosure of AI use and audience trust. If the content is of high perceived quality, with the help of AI, it may be able to mitigate the negative trust effect of the use of AI, but only if the creator has a consistent personal voice. In the trust function, comparative statics show that disclosure has a nonlinear effect on the marginal effect of disclosure. The results not only provide theoretical insights for research into communication in the context of AI but also serve as a guide for self-media creators dealing with the integration of generative AI tools.

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