hblim/top_reddit_posts_daily

Top Reddit Posts Daily

Dataset Summary

A continuously-updated snapshot of public Reddit discourse on AI news. Each night a GitHub Actions cron job

1. Scrapes new submissions from a configurable list of subreddits (→ data_raw/)
2. Classifies each post with a DistilBERT sentiment model served on Replicate (→ data_scored/)
3. Summarises daily trends for lightweight front-end consumption (→ daily_summary/)

The result is an easy-to-query, time-stamped record of Reddit sentiment that can be used for NLP research, social-media trend analysis, or as a teaching dataset for end-to-end MLOps.

Source code https://github.com/halstonblim/reddit_sentiment_pipeline

Currently configured to scrape only the top daily posts and comments to respect rate limits

subreddits:
  - name: artificial
    post_limit: 100
    comment_limit: 10
  - name: LocalLLaMA
    post_limit: 100
    comment_limit: 10
  - name: singularity
    post_limit: 100
    comment_limit: 10
  - name: OpenAI
    post_limit: 100
    comment_limit: 10

Supported Tasks

This dataset can be used for:

• Text classification (e.g., sentiment analysis)
• Topic modeling
• Language generation and summarization
• Time‑series analysis of Reddit activity

Languages

• English, no filtering is currently done on the raw text

Dataset Structure

hblim/top_reddit_posts_daily/
└── data_raw/                     # contains raw data scraped from reddit
    ├── 2025‑05‑01.parquet
    ├── 2025‑05‑01.parquet
    └── …
└── data_scored/                  # contains same rows as raw data but with sentiment scores
    ├── 2025‑05‑01.parquet
    ├── 2025‑05‑01.parquet
    └── …
└── subreddit_daily_summary.csv/  # contains daily summaries of sentiment averages grouped by (day, subreddit)

Data Fields

Name	Type	Description
subreddit	string	Name of the subreddit (e.g. “GooglePixel”)
created_at	datetime	UTC timestamp when the post/comment was originally created
retrieved_at	datetime	Local timezone timestamp when this data was scraped
type	string	"post" or "comment"
text	string	For posts: title + "\n\n" + selftext; for comments: comment body
score	int	Reddit score (upvotes – downvotes)
post_id	string	Unique Reddit ID for the post or comment
parent_id	string	For comments: the parent comment/post ID; null for top‑level posts

Example entry:

Field	Value
subreddit	apple
created_at	2025-04-17 19:59:44-05:00
retrieved_at	2025-04-18 12:46:10.631577-05:00
type	post
text	Apple wanted people to vibe code Vision Pro apps with Siri
score	427
post_id	1k1sn9w
parent_id	None

Data Splits

There are no explicit train/test splits. Data is organized by date under the data_raw/ or data_scored/ folder.

Dataset Creation

• A Python script (scrape.py) runs daily, fetching the top N posts and top M comments per subreddit.
• Posts are retrieved via PRAW’s subreddit.top(time_filter="day").
• Data is de‑duplicated against the previous day’s post_id values.
• Stored as Parquet under data_raw/{YYYY‑MM‑DD}.parquet.

License

This dataset is released under the MIT License.

Citation

If you use this dataset, please cite it as:

@misc{lim_top_reddit_posts_daily_2025,
  title        = {Top Reddit Posts Daily: Scraped Daily Top Posts and Comments from Subreddits},
  author       = {Halston Lim},
  year         = {2025},
  publisher    = {Hugging Face Datasets},
  howpublished = {\url{https://huggingface.co/datasets/hblim/top_reddit_posts_daily}}
}

Usage Example

Example A: Download and load a single day via HF Hub

from huggingface_hub import HfApi
import pandas as pd

api = HfApi()
repo_id = "hblim/top_reddit_posts_daily"

date_str = "2025-04-18"
today_path = api.hf_hub_download(
    repo_id=repo_id,
    filename=f"data_raw/{date_str}.parquet",
    repo_type="dataset"
)
df_today = pd.read_parquet(today_path)
print(f"Records for {date_str}:")
print(df_today.head())

Example B: List, download, and concatenate all days

from huggingface_hub import HfApi
import pandas as pd

api = HfApi()
repo_id = "hblim/top_reddit_posts_daily"

# 1. List all parquet files in the dataset repo
all_files = api.list_repo_files(repo_id, repo_type="dataset")
parquet_files = sorted([f for f in all_files if f.startswith("data_raw/") and f.endswith(".parquet")])

# 2. Download each shard and load with pandas
dfs = []
for shard in parquet_files:
    local_path = api.hf_hub_download(repo_id=repo_id, filename=shard, repo_type="dataset")
    dfs.append(pd.read_parquet(local_path))

# 3. Concatenate into one DataFrame
df_all = pd.concat(dfs, ignore_index=True)
print(f"Total records across {len(dfs)} days: {len(df_all)}")

Limitations & Ethics

• Bias: Data reflects Reddit’s user base and community norms, which may not generalize.
• Privacy: Only public content is collected; no personally identifiable information is stored.