Datasets:

shoni
/

farcaster

license: cc-by-4.0
pretty_name: Farcaster Open Social Dataset
tags:
  - social-media
  - farcaster
  - farcaster-threads
  - tabular
  - parquet
  - datasets
language: []
task_categories:
  - text-retrieval

Farcaster Threads Dataset

Overview

This dataset contains high-quality thread data from Farcaster's entire history, featuring 512-dimensional embeddings generated using VoyageAI (float32) on formatted thread text. The dataset includes comprehensive thread metadata, engagement metrics, and vector embeddings suitable for semantic search, recommendation systems, and content analysis.

Dataset Details

Total Records: ~20,182,407 threads
Data Cutoff: 2025-08-02 02:20:49.000 +0800 (no threads newer than this date)
Quality Filter: Non-spam content only (spam label = 2), low-effort replies and threads removed.
Embedding Model: VoyageAI 512-dimensional float32 vectors
Repository: shoni/farcaster
Data Location: /threads folder

Schema

Column	Type	Description
`hash`	bytes	Unique thread identifier (primary key)
`fids`	list[int]	FIDs of reply authors, ordered by reaction count (most reacted first)
`reactions`	int	Total reaction count from non-spam users
`author_fid`	int	FID of the original thread author
`timestamp`	timestamp	Thread creation date
`claimed_at`	timestamp	When thread was claimed by processing worker
`blob`	string	Formatted thread text including replies and author information
`blob_embedding`	list[float32]	512-dimensional VoyageAI embeddings of the blob text
`blob_timestamp`	timestamp	Timestamp of when the blob text was generated
`blob_embedding_binary`	bytes	Binary representation of the embeddings (512 bits)

Data Quality

Spam Filtering: Only includes threads with spam label = 2 (verified non-spam)
Engagement Metrics: Reaction counts filtered to exclude spam users
Historical Coverage: Complete Farcaster thread history up to cutoff date
Reply Ordering: Reply authors (fids) sorted by engagement for relevance

Use Cases

Semantic search over Farcaster content
Thread recommendation systems
Content similarity analysis
Social network analysis
Engagement prediction modeling
Community detection and analysis

Loading the Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("shoni/farcaster", data_dir="threads")

# Access embeddings
embeddings = dataset['train']['blob_embedding']

# Access formatted thread text
threads = dataset['train']['blob']

Citation

If you use this dataset in your research, please cite:

@dataset{farcaster_open_social_dataset_2025,
  title={Farcaster Open Social Dataset},
  author={shoni},
  year={2025},
  url={https://huggingface.co/datasets/shoni/farcaster}
}

License

CC-BY-4.0

Example Thread

Note: The actual blob_embedding content in the dataset uses private formatting techniques that slightly differ from the displayed blob text in the dataset. The displayed blob text is a human-readable format, replies are ordered from most to least popular (as whole branches):

@keremgurel: Beta launch is set for April 24th - exactly 3 months after the project kickoff. We’re building the first truly intuitive no-code website builder powered by blockchain, on @base https://imagedelivery.net/BXluQx4ige9GuW0Ia56BHw/5a93839e-f6be-4c04-b66a-4c2783328600/original https://stream.warpcast.com/v1/video/0195eca0-eccd-3042-b676-2f1bbd179695.m3u8
~1: @compusophy: cool!! though no need to use the word first here
~2: @keremgurel: Thanks! Why so? To my knowledge there aren’t any other onchain website builders out there
~1: @damag.eth: where can i sign up 👀
~2: @keremgurel: https://onchainsite.xyz https://onchainsite.xyz/

Repository

https://huggingface.co/datasets/shoni/farcaster