# The Value of AI-Generated Metadata for UGC Platforms: Evidence from a Large-scale Field Experiment

arXiv: https://arxiv.org/abs/2412.18337 · HTML: https://mlsanity.com/paper/2412.18337/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata](https://mlsanity.com/paper/2309.08787/) (2023)

## Built on by (most cited first, 5 of 5)

- [Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act](https://mlsanity.com/paper/2506.01931/) (2025)
- [AIGVE-MACS: Unified Multi-Aspect Commenting and Scoring Model for AI-Generated Video Evaluation](https://mlsanity.com/paper/2507.01255/) (2025)
- [Semi-Supervised Synthetic Data Generation with Fine-Grained Relevance Control for Short Video Search Relevance Modeling](https://mlsanity.com/paper/2509.16717/) (2025)
- [Improving Behavioral Alignment in LLM Social Simulations via Context Formation and Navigation](https://mlsanity.com/paper/2601.01546/) (2026)
- [Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning](https://mlsanity.com/paper/2605.15299/) (2026)

## Abstract (arXiv)

AI-generated content (AIGC), such as advertisement copy, product descriptions, and social media posts, is becoming ubiquitous in business practices. However, the value of AI-generated metadata, such as titles, remains unclear on user-generated content (UGC) platforms. To address this gap, we conducted a large-scale field experiment on a leading short-video platform in Asia to provide about 1 million users access to AI-generated titles for their uploaded videos. Our findings show that the provision of AI-generated titles significantly boosted content consumption, increasing valid watches by 1.6% and watch duration by 0.9%. When producers adopted these titles, these increases jumped to 7.1% and 4.1%, respectively. This viewership-boost effect was largely attributed to the use of this generative AI (GAI) tool increasing the likelihood of videos having a title by 41.4%. The effect was more pronounced for groups more affected by metadata sparsity. Mechanism analysis revealed that AI-generated metadata improved user-video matching accuracy in the platform's recommender system. Interestingly, for a video for which the producer would have posted a title anyway, adopting the AI-generated title decreased its viewership on average, implying that AI-generated titles may be of lower quality than human-generated ones. However, when producers chose to co-create with GAI and significantly revised the AI-generated titles, the videos outperformed their counterparts with either fully AI-generated or human-generated titles, showcasing the benefits of human-AI co-creation. This study highlights the value of AI-generated metadata and human-AI metadata co-creation in enhancing user-content matching and content consumption for UGC platforms.