Made with AI: Consumer Engagement with Social Media Containing AI Disclosures

6 citations

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.

6
Citations
Research
Paper Only

Study specs

Analysis of TikTok engagement data following AIGC disclosure implementation, supplemented by six preregistered experiments.

Discipline
Social Science
Study Type
Experimental Study
Year
2025
Human Data Platform
Prolific

Measured Outcomes

Impact of AIGC disclosures on consumer engagement and the mediating role of parasocial connections.

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

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