Datasets:

IndexTeam
/

CASTER-Bench

	---
	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
	gated: "manual"
	extra_gated_heading: "Access Request for CASTER-Bench"
	extra_gated_prompt: "Please provide your academic or corporate affiliation and a detailed reason for your request. To speed up verification, we highly recommend registering your Hugging Face account with your institutional email (e.g., .edu, .ac, or company domains). Applications with vague reasons (e.g., 'test', 'study') will be rejected."
	extra_gated_fields:
	Company / Affiliation: "text"
	Detailed Justification / Research Purpose: "text"
	---

	# CASTER-Bench

	[![Demo](https://img.shields.io/badge/Demo-Online-orange.svg)](https://vowel-bagging-tweed.ngrok-free.dev/?token=acl2026caster)
	[![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/).