---
license: cc-by-nc-4.0
task_categories:
  - video-classification
  - text-classification
language:
  - zh
size_categories:
  - 1K<n<10K
tags:
  - video-quality-assessment
  - user-generated-content
  - social-reasoning
  - community-resonance
  - multimodal
configs:
  - config_name: default
    data_files:
      - path: CASTER_Bench.json
        split: test
---

# CASTER-Bench

[![Paper](https://img.shields.io/badge/arXiv-CASTER-b31b1b.svg)](https://arxiv.org/abs/2606.01897)
[![Code](https://img.shields.io/badge/GitHub-medea__rl-yellow.svg)](https://github.com/bilibili/medea_rl)
[![Model](https://img.shields.io/badge/HuggingFace-MEDEA-blue.svg)](https://huggingface.co/IndexTeam/MEDEA)
[![Dataset](https://img.shields.io/badge/HuggingFace-CASTER--Bench-green.svg)](https://huggingface.co/datasets/IndexTeam/CASTER-Bench)

**CASTER-Bench** is a human-annotated multimodal benchmark for **Community-Aware Assessment of Social Textual Engagement and Resonance (CASTER)** — a task that evaluates whether User-Generated Content (UGC) achieves positive community resonance, going beyond traditional aesthetic-focused Video Quality Assessment (VQA).

This benchmark is introduced in the ACL 2026 paper: *"Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation"*.

## Motivation

Traditional VQA focuses on pixel-level integrity and low-level visual cues, which fails to capture how quality is actually perceived on UGC platforms. CASTER redefines UGC quality through the lens of **social reasoning** — assessing whether content elicits genuine community resonance based on its multimodal attributes (title, tags, video content, cover image) rather than visual quality alone.

## Dataset Description

CASTER-Bench contains **1,485 long-form UGC videos** (average duration: 442 seconds, total: 182.5 hours) sourced from Bilibili, covering 30 major content categories with balanced representation. Unlike existing VQA datasets that rely on short clips (8-10s), CASTER-Bench enables evaluation of narrative coherence, information density, and sustained engagement.

### Key Features

- **Long-form videos**: Average 442s duration (vs. 8-20s in prior VQA benchmarks)
- **Rich multimodal signals**: Title, tags, video content, cover image, category metadata
- **Expert annotations**: Labeled by trained content moderators using a human-centered rubric grounded in real community feedback
- **Diverse categories**: 30 top-level categories, 166 sub-categories

### Quality Label Distribution

| Label | Chinese | Count | Percentage |
|-------|---------|-------|-----------|
| Excellent | 优 | 158 | 10.6% |
| Good | 良 | 253 | 17.0% |
| Average | 中 | 573 | 38.6% |
| Poor | 差 | 501 | 33.7% |

This distribution mirrors real-world platforms, presenting a realistic challenge for identifying high-quality content amidst massive amounts of average data.

### Categories

Gaming, Knowledge, Entertainment, Vlog, Food, Music, Dance, Sports, Fitness, Animals, Anime & Manga, Film & TV, Fashion & Beauty, Tech & Digital, Automotive, Travel, Agriculture, Parenting, Healthcare, Handicrafts, Painting, Home & Property, Short Skits, Relationship, Outdoor, Meme Video, Mysticism, Artificial Intelligence, Hobbies, Life Tips

## Dataset Structure

```
CASTER-Bench/
├── README.md
├── CASTER_Bench.json    # Annotations (1,485 entries)
├── cover.zip            # Video cover images
└── video.zip            # Video files
```

### Annotation Format

Each entry in `CASTER_Bench.json`:

```json
{
  "archive_title": "Video title",
  "tag": "Comma-separated content tags",
  "new_tid_name": "Top-level category",
  "new_sub_tid_name": "Sub-category",
  "video_path": "video/xxxxx.mp4",
  "cover_path": "cover/xxxxx.jpg",
  "human_label": "优/良/中/差"
}
```

## Usage

```python
import json
from huggingface_hub import hf_hub_download

# Download annotations
path = hf_hub_download(repo_id="IndexTeam/CASTER-Bench", filename="CASTER_Bench.json", repo_type="dataset")
with open(path) as f:
    data = json.load(f)

# Download videos and covers
video_zip = hf_hub_download(repo_id="IndexTeam/CASTER-Bench", filename="video.zip", repo_type="dataset")
cover_zip = hf_hub_download(repo_id="IndexTeam/CASTER-Bench", filename="cover.zip", repo_type="dataset")
```

## Comparison with Existing Benchmarks

| Dataset | Videos | Avg Duration | Total Duration | Focus | Modality |
|---------|--------|-------------|---------------|-------|----------|
| KoNViD-1k | 1,200 | 8s | 2.7h | Aesthetic & Technical | Video Only |
| LIVE-VQC | 585 | 10s | 1.6h | Aesthetic & Technical | Video Only |
| YouTube-UGC | 1,380 | 20s | 7.7h | Aesthetic & Technical | Video Only |
| FineVD | 6,104 | 8s | 13.6h | Aesthetic & Technical | Video Only |
| **CASTER-Bench** | **1,485** | **442s** | **182.5h** | **Social & Content** | **Title+Tags+Cover+Video** |

## Citation

```bibtex
@article{li2026community,
  title={Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation},
  author={Li, Tianjiao and Zhao, Kai and Li, Xiang and Liu, Yang and Sun, Huyang},
  journal={arXiv preprint arXiv:2606.01897},
  year={2026}
}
```

## License

This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).