User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Sora
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Abstract (arXiv)
As AI-generated video platforms rapidly advance, ethical challenges such as copyright infringement emerge. This study examines how users make sense of AI-generated videos on OpenAI's Sora by conducting a qualitative content analysis of user comments. Through a thematic analysis, we identified four dynamics that characterize how users negotiate authenticity, authorship, and platform governance on Sora. First, users acted as critical evaluators of realism, assessing micro-details such as lighting, shadows, fluid motion, and physics to judge whether AI-generated scenes could plausibly exist. Second, users increasingly shifted from passive viewers to active creators, expressing curiosity about prompts, techniques, and creative processes. Text prompts were perceived as intellectual property, generating concerns about plagiarism and remixing norms. Third, users reported blurred boundaries between real and synthetic media, worried about misinformation, and even questioned the authenticity of other commenters, suspecting bot-generated engagement. Fourth, users contested platform governance: some perceived moderation as inconsistent or opaque, while others shared tactics for evading prompt censorship through misspellings, alternative phrasing, emojis, or other languages. Despite this, many users also enforced ethical norms by discouraging the misuse of real people's images or disrespectful content. Together, these patterns highlighted how AI-mediated platforms complicate notions of reality, creativity, and rule-making in emerging digital ecosystems. Based on the findings, we discuss governance challenges in Sora and how user negotiations inform future platform governance.
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References · 10
- 2024Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Modelscited by 477
- 2023Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Promptingcited by 36
- 2023PromptCARE: Prompt Copyright Protection by Watermark Injection and Verificationcited by 32
- 2024Understanding the Impact of AI Generated Content on Social Media: The Pixiv Casecited by 13
- 2024Sora OpenAI's Prelude: Social Media Perspectives on Sora OpenAI and the Future of AI Video Generationcited by 8
- 2025The Impact of Generative AI on Social Media: An Experimental Studycited by 6
- 2025Characterizing AI-Generated Misinformation on Social Mediacited by 5
- 2023Protect Your Prompts: Protocols for IP Protection in LLM Applicationscited by 4
- 2024"Sora is Incredible and Scary": Emerging Governance Challenges of Text-to-Video Generative AI Modelscited by 3
- 2025Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Mediacited by 3
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