pretty_name: Social Vision and Language Dataset (SVLD)
license: other
language:
- en
tags:
- multimodal
- vision-language
- social-media
- image
- video
- text
- comment-trees
- popularity-prediction
- arxiv:2006.08335
- datasets
task_categories:
- image-text-to-text
- image-to-text
- video-text-to-text
- image-classification
- text-classification
- visual-question-answering
- tabular-regression
size_categories:
- 1M<n<10M
Social Vision and Language Dataset (SVLD)
Original Paper: A Dataset and Benchmarks for Multimedia Social Analysis (2020)
Authors: Bofan Xue, David Chan, John Canny
Institution: University of California, Berkeley
📌 Overview
The Social Vision and Language Dataset (SVLD) is a large-scale multimodal social media dataset designed to support research in:
- Vision–language modeling
- Multimodal fusion
- Social signal prediction
- Comment-tree modeling
- Temporal social dynamics
- Content popularity prediction
SVLD combines images, videos, text, social engagement signals, and full comment trees within the same context, enabling joint modeling across modalities in realistic, in-the-wild social media settings.
📦 Current Release (S3 Shard Edition)
The dataset is currently distributed as:
1961 daily shards
Each shard corresponds approximately to one day of collected data.
⚠ Important Notice
Due to long-term storage issues and partial data corruption:
- This release may not contain the full original dataset
- Some days, posts, media files, or metadata may be missing
- Total dataset size may vary
Researchers are strongly encouraged to:
- Recompute dataset statistics locally
- Avoid assuming counts from the original publication
- Design pipelines that tolerate partial or missing data
🧩 Dataset Structure
Each Post May Contain
- One or more images
- One or more videos
- Optional per-media descriptions
- A natural language title
- User-provided tags
- Social signals (upvotes, downvotes, favorites, views)
- Timestamp
- A full comment forest
Each Comment May Contain
- Text
- Images
- GIFs or videos
- Recursive replies (tree structure)
🎯 Modalities
SVLD supports research across:
- Images (posts + comments)
- Videos (posts + comments)
- Text (titles, descriptions, comments)
- Social Metrics (votes, favorites, views)
- Tags (user-generated)
- Tree Structure (comment forests)
- Temporal Data (timestamps)
🔬 Research Directions
SVLD enables work in:
- Multimodal fusion architectures
- Image/video + language modeling
- Popularity and engagement prediction
- Social dynamics modeling
- Tag and metadata prediction
- Comment tree reasoning
- Temporal distribution analysis
- Multimodal retrieval
- Content moderation research
⚙ Data Quality Notes
- Some media files may be unavailable
- Some shards may be incomplete
- Social metrics reflect snapshot-at-scrape time
- Engagement distributions are heavily long-tailed
- Content reflects real-world social media (unfiltered, in-the-wild)
📖 Citation
If you use SVLD, please cite:
Xue, B., Chan, D., & Canny, J. (2020).
A Dataset and Benchmarks for Multimedia Social Analysis.
arXiv:2006.08335
📜 License & Usage
This dataset is intended for academic research use only.
Users are responsible for complying with platform terms and ethical research standards.