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---
license: mit
dataset_info:
features:
- name: subreddit
dtype: string
- name: created_at
dtype: timestamp[ns, tz=US/Central]
- name: retrieved_at
dtype: timestamp[ns, tz=US/Central]
- name: type
dtype: string
- name: text
dtype: string
- name: score
dtype: int64
- name: post_id
dtype: string
- name: parent_id
dtype: string
---
# 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](https://opensource.org/licenses/MIT).
## Citation
If you use this dataset, please cite it as:
```bibtex
@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
```python
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
```python
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. |