Abstract
The media landscape now contains a growing mix of real and synthetic videos, presenting either authentic or false content. Drawing on the heuristic–systematic model, we conducted a mixed-design survey experiment in China to examine how perceived technical quality and content familiarity influence individuals’ perceptions of and performance in detecting real and synthetic videos. Multilevel analyses based on participant evaluations revealed that both perceived technical quality and content familiarity were positively associated with perceived video realness and trust in the vlogger. Higher perceived technical quality improved detection accuracy for real videos but decreased accuracy for synthetic videos. In addition, the positive association between content familiarity and trust in the vlogger was stronger for synthetic videos than for real videos. These findings deepen our understanding of how heuristic processing shapes perceptions and discernment of potentially synthetic videos and offer practical insights for mitigating the risks associated with AI-generated visual deepfakes. © The Author(s) 2025.
| Original language | English |
|---|---|
| Number of pages | 13 |
| Journal | Social Media and Society |
| Volume | 11 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Oct 2025 |
Bibliographical note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially supported by the University Grants Committee of Hong Kong (Project No. 6000856) and City University of Hong Kong (Project No. 7005700). Open access made possible with partial support from the Open Access Publishing Fund of City University of Hong Kong.
Research Keywords
- synthetic videos
- perceived technical quality
- content familiarity
- heuristic-systematic model
- detection accuracy
- video realness
Publisher's Copyright Statement
- This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/
Fingerprint
Dive into the research topics of 'What Looks Good and Familiar Seems Real: How Heuristic Processing of AI-Powered Synthetic and Real Videos Shapes User Perceptions and Detection Accuracy'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver