# Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media

arXiv: https://arxiv.org/abs/2503.05711 · HTML: https://mlsanity.com/paper/2503.05711/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Automated Fact Checking in the News Room](https://mlsanity.com/paper/1904.02037/) (2019)
- [Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions](https://mlsanity.com/paper/2303.12118/) (2023)
- [Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation](https://mlsanity.com/paper/2203.00710/) (2022)
- [The Landscape of User-centered Misinformation Interventions -- A Systematic Literature Review](https://mlsanity.com/paper/2301.06517/) (2023)
- [Designing Transparency Cues in Online News Platforms to Promote Trust: Journalists' & Consumers' Perspectives](https://mlsanity.com/paper/2108.02325/) (2021)
- [Effects of Automated Misinformation Warning Labels on the Intents to Like, Comment and Share Posts](https://mlsanity.com/paper/2403.12916/) (2024)

## Built on by (most cited first, 9 of 9)

- [Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation](https://mlsanity.com/paper/2506.07211/) (2025)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers' Trust](https://mlsanity.com/paper/2601.09620/) (2026)
- [Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News](https://mlsanity.com/paper/2605.14999/) (2026)
- [Creating Disability Story Videos with Generative AI: Motivation, Expression, and Sharing](https://mlsanity.com/paper/2601.12617/) (2026)
- [When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platform](https://mlsanity.com/paper/2601.18275/) (2026)
- ["AI Watermarking": Bridging Policy Discourse and Technical Capabilities](https://mlsanity.com/paper/2606.28331/) (2026)
- [Understanding Security and Privacy Perceptions of Content Creators Regarding AI Labels of AI-Generated Content](https://mlsanity.com/paper/2608.08129/) (2026)
- [Trustworthy synthetic visual media: Evidence across the media lifecycle](https://mlsanity.com/paper/2609.34232/) (2026)

## Abstract (arXiv)

In this research, we explored the efficacy of various warning label designs for AI-generated content on social media platforms e.g., deepfakes. We devised and assessed ten distinct label design samples that varied across the dimensions of sentiment, color/iconography, positioning, and level of detail. Our experimental study involved 911 participants randomly assigned to these ten label designs and a control group evaluating social media content. We explored their perceptions relating to 1. Belief in the content being AI-generated, 2. Trust in the labels and 3. Social Media engagement perceptions of the content. The results demonstrate that the presence of labels had a significant effect on the users belief that the content is AI generated, deepfake, or edited by AI. However their trust in the label significantly varied based on the label design. Notably, having labels did not significantly change their engagement behaviors, such as like, comment, and sharing. However, there were significant differences in engagement based on content type: political and entertainment. This investigation contributes to the field of human computer interaction by defining a design space for label implementation and providing empirical support for the strategic use of labels to mitigate the risks associated with synthetically generated media.