Datasets:
Formats:
json
Languages:
Chinese
Size:
1K - 10K
Tags:
video-quality-assessment
user-generated-content
social-reasoning
community-resonance
multimodal
License:
| 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 | |
| **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 | |
| @inproceedings{li2026caster, | |
| 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}, | |
| booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL)}, | |
| year={2026} | |
| } | |
| ``` | |
| ## License | |
| This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). | |