# Effects of Automated Misinformation Warning Labels on the Intents to Like, Comment and Share Posts

arXiv: https://arxiv.org/abs/2403.12916 · HTML: https://mlsanity.com/paper/2403.12916/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media](https://mlsanity.com/paper/2503.05711/) (2025)
- [Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals](https://mlsanity.com/paper/2505.16057/) (2025)

## Abstract (arXiv)

With fact-checking by professionals being difficult to scale on social media, algorithmic techniques have been considered. However, it is uncertain how the public may react to labels by automated fact-checkers. In this study, we investigate the use of automated warning labels derived from misinformation detection literature and investigate their effects on three forms of post engagement. Focusing on political posts, we also consider how partisanship affects engagement. In a two-phases within-subjects experiment with 200 participants, we found that the generic warnings suppressed intents to comment on and share posts, but not on the intent to like them. Furthermore, when different reasons for the labels were provided, their effects on post engagement were inconsistent, suggesting that the reasons could have undesirably motivated engagement instead. Partisanship effects were observed across the labels with higher engagement for politically congruent posts. We discuss the implications on the design and use of automated warning labels.