---
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.