Made with AI: Consumer Engagement with Social Media Containing AI Disclosures
Abstract
Social media shapes how people connect, communicate and consume information. As generative artificial intelligence (AI) becomes an increasingly common tool for content creation, many platforms have introduced disclosure requirements to inform consumers when content has been created or significantly edited by AI. Yet, little is known about how such AI-generated content (AIGC) disclosures influence consumer engagement—a key metric for creators, platforms, and brands—in part due to the unique setting of social media relative to other examinations of responses to AI. This research examines whether and why AIGC disclosures affect engagement on social media. Analysis of engagement behavior on TikTok following the introduction of AIGC disclosures and six preregistered experiments find that disclosures reduce consumer engagement. This reduction does not stem from content-related explanations such as lower perceived quality or concerns about manipulation. Instead, we identify a novel process: AIGC disclosures reduce parasocial connection—one-sided emotional bonds between consumers and creators—by signaling reduced effort from the creator. As such, disclosures that signal greater effort can mitigate reductions in engagement. We discuss the implications of these findings for platform policy, content creator strategy, and the future design of AI disclosure practices.
Study specs
Analysis of TikTok engagement data following AIGC disclosure implementation, supplemented by six preregistered experiments.
- Institution
- University of Southern California
- Discipline
- Social Science
- Study Type
- Experimental Study
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
Impact of AIGC disclosures on consumer engagement and the mediating role of parasocial connections.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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