---
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.