anhchanghoangsg/reddit_pushshift_dataset_cleaned

🚀 Cleaned Reddit Pushshift Dataset (Parquet)

📖 Dataset Description

This dataset is a highly optimized, meticulously cleaned, and structured version of the raw Reddit Pushshift dump. It has been transformed from massive .zst compressed JSON files into highly efficient, columnar Parquet files, making it immediately ready for Big Data analytics, SQL querying (via DuckDB), and Large Language Model (LLM) training.

The dataset includes both Reddit Submissions (Posts) and Comments.

🧹 Data Cleaning & Preprocessing Pipeline

Unlike the raw Pushshift dump which contains heavy noise, this dataset has been processed through a strict ETL pipeline:

1. Dead Data Removal: - Completely dropped rows where the author, selftext, or body is [deleted], [removed], or None.
   • Dropped comments missing essential relation links (link_id, parent_id).
2. Noise & Encoding Fixes:
   • Filtered out submissions containing severe Unicode/font errors (e.g., \ufffd characters).
3. Data Type Normalization:
   • Converted raw Unix timestamps (created_utc) into readable YYYY-MM-DD string format.
   • Safely cast numerical fields (score, num_comments, upvote_ratio, etc.) to strict Integer/Float types to prevent schema crashing.
   • Handled missing text fields by filling them with "None" to ensure consistent string types.
4. NSFW & Sensitive Content Filtering:
   • Applied a smart Regex and CamelCase keyword filter to automatically identify and exclude highly sensitive or purely NSFW subreddits from the repository.
5. Storage Optimization (The "Small File" Fix):
   • Micro-files (under 1MB) representing inactive subreddits were skipped. This prevents the "Small File Problem" in Parquet storage, ensuring the Hugging Face Data Viewer and distributed computing frameworks (like Apache Spark) run at lightning speed.

🗂️ Dataset Structure (Schema)

📝 Submissions (Posts)

Field	Type	Description
author	string	The Reddit username of the poster.
name	string	Unique base36 ID of the submission (e.g., t3_xxx).
title	string	The title of the post.
selftext	string	The text body of the post.
created_utc	string	Date of creation (YYYY-MM-DD).
score	int64	Net upvotes minus downvotes.
upvote_ratio	float64	Ratio of upvotes to total votes.
num_comments	int64	Number of comments on the thread.
num_crossposts	int64	Number of times the post was crossposted.
subreddit	string	Name of the subreddit.
subreddit_id	string	Unique ID of the subreddit.
subreddit_subscribers	int64	Number of members in the subreddit.
domain	string	The domain of the link submitted (if any).
crosspost_parent	string	ID of the parent post if this is a crosspost.

💬 Comments

Field	Type	Description
author	string	The Reddit username of the commenter.
name	string	Unique base36 ID of the comment (e.g., t1_xxx).
body	string	The text content of the comment.
created_utc	string	Date of creation (YYYY-MM-DD).
score	int64	Net upvotes minus downvotes.
controversiality	int64	Reddit's controversiality metric (0 or 1).
parent_id	string	The ID of the comment/post this is replying to.
link_id	string	The ID of the parent submission.
subreddit	string	Name of the subreddit.
subreddit_id	string	Unique ID of the subreddit.

💻 How to Use

You can easily load this dataset using the Hugging Face datasets library. Since it's stored in Parquet, it will stream efficiently.

from datasets import load_dataset

# Load the dataset (Streaming mode is recommended due to massive size)
dataset = load_dataset("anhchanghoangsg/reddit_pushshift_dataset_cleaned", streaming=True)

# Print the first row of the train split
print(next(iter(dataset['train'])))