Pandita-IA/reddit-refractive-surgery

Dataset Card for Reddit LASIK Communities Dataset

Dataset Summary

This dataset contains posts and comments from two LASIK-related Reddit communities: r/lasik and r/LasikSupport. It was created to support research on patient experiences, community support dynamics, and medical discourse in online health communities.

The dataset includes approximately 1,983 posts (993 from r/lasik, 990 from r/LasikSupport) and +27000 comments from the same communities. It captures discussions about laser eye surgery experiences, pre-operative concerns, post-operative outcomes, and peer support exchanges.

Primary use cases:

• —Sentiment analysis of patient experiences
• —Topic modeling of healthcare discussions
• —Natural language processing research on informal medical discourse
• —Longitudinal studies of online health communities
• —Analysis of patient decision-making and information-seeking behavior

Languages: English

Dataset Structure

Data Instances

The dataset consists of two separate CSV files:

Posts dataset example:

{
  "post_id": "abc123",
  "subreddit": "lasik",
  "author": "username",
  "title": "2 weeks post-op update",
  "selftext": "My experience so far...",
  "score": 15,
  "num_comments": 8,
  "created_utc": 123456789
}

Comments dataset example:

{
  "comment_id": "def456",
  "post_id": "abc123",
  "author": "username",
  "body": "Thanks for sharing your experience...",
  "score": 5,
  "depth": 1,
  "created_utc": 123456789
}

Data Fields

Posts Dataset

• —post_id: Unique identifier for the post
• —subreddit: Source subreddit (lasik or LasikSupport)
• —author: Reddit username (pseudonym)
• —title: Post title
• —selftext: Post body text
• —url: External URL (if link post)
• —score: Net upvote score
• —upvote_ratio: Ratio of upvotes to total votes
• —num_comments: Number of comments on the post
• —created_utc: Unix timestamp
• —permalink: Reddit URL to the post
• —link_flair_text: Post flair/category
• —edited: Boolean indicating if edited
• —locked: Boolean indicating if locked

Comments Dataset

• —comment_id: Unique identifier for the comment
• —post_id: ID of parent post
• —parent_id: ID of parent comment (if reply)
• —author: Reddit username (pseudonym)
• —body: Comment text content
• —score: Net upvote score
• —created_utc: Unix timestamp
• —edited: Boolean indicating if edited
• —depth: Comment nesting level in thread
• —controversiality: Reddit controversy score
• —permalink: Reddit URL to the comment
• —is_submitter: Boolean indicating if author is original poster

Data Splits

No predefined splits are provided. Researchers can create custom splits based on:

• —Temporal: Pre/post specific dates (e.g., 2020)
• —Community: r/lasik vs r/LasikSupport
• —Random: Standard train/validation/test splits for ML tasks

Dataset Creation

Source Data

Data Collection

The data was collected from publicly available Reddit posts and comments using the Reddit API and/or Pushshift API. The collection targeted two subreddits:

• —r/lasik: General LASIK discussion community
• —r/LasikSupport: Support-focused community for LASIK patients

Collection timeframe: [Insert dates, e.g., 2024-2025]

Collection method: Automated retrieval via Python scripts using API wrappers (praw/Pushshift)

Data Processing

Preprocessing steps:

1. 1.Converted Unix timestamps to human-readable dates
2. 2.Removed exact duplicate entries
3. 3.Preserved original text without spelling/grammar corrections
4. 4.Standardized metadata fields across subreddits
5. 5.Retained deleted/removed content markers

Software used: Python 3.x with pandas, praw

Annotations

No manual annotations are included. Labels for sentiment, topics, or other tasks should be derived programmatically or added by researchers.

Considerations for Using the Data

Social Impact and Biases

• —Self-selection bias: Users who post online may not represent all LASIK patients
• —Platform bias: Reddit demographics skew younger and more tech-savvy
• —Negativity bias: Users with complications may be overrepresented
• —Temporal bias: Medical practices and outcomes evolve over time
• —Geographic bias: Predominantly English-speaking, likely US-centric

Discussion of Biases

The dataset reflects experiences shared voluntarily on Reddit and may overrepresent negative outcomes, as individuals experiencing complications often seek online support. Positive outcomes may be underreported. The communities have different purposes (r/lasik is general, r/LasikSupport is support-focused), which may affect content tone.

Personal and Sensitive Information

• —All data is publicly available on Reddit
• —Usernames are pseudonyms but not additionally anonymized
• —Posts and comments may contain self-disclosed personal medical information
• —Some content includes emotional testimonies about negative surgical outcomes
• —Privacy consideration: While data is public, researchers should avoid attempting to reidentify users

Limitations

• —Missing data: Deleted/removed content appears as empty body or selftext fields
• —Duplicates: Edited content may appear multiple times if retrieved at different timestamps
• —External links: URLs in content may no longer be active
• —No ground truth: Medical accuracy of claims is not verified
• —Temporal coverage: May have gaps depending on API availability

How to Use

Loading the Dataset

from datasets import load_dataset

# Load posts dataset
posts = load_dataset("Pandita-IA/reddit-refractive-surgery", data_files="posts.csv")

# Load comments dataset
comments = load_dataset("Pandita-IA/reddit-refractive-surgery", data_files="comments.csv")

# Or load both
dataset = load_dataset("Pandita-IA/reddit-refractive-surgery")

Using with Pandas

import pandas as pd

# Load locally
posts_df = pd.read_csv("posts.csv")
comments_df = pd.read_csv("comments.csv")

# View sample data
print(posts_df[['title', 'score', 'created_date']].head())
print(comments_df[['body', 'score', 'depth']].head())

Example: Sentiment Analysis Setup

from datasets import load_dataset
from transformers import pipeline

# Load dataset
comments = load_dataset("Pandita-IA/reddit-refractive-surgery", data_files="comments.csv", split="train")

# Initialize sentiment analyzer
sentiment_analyzer = pipeline("sentiment-analysis")

# Analyze sample comments
sample = comments.filter(lambda x: x['body'] and len(x['body']) > 50).select(range(10))
for comment in sample:
    result = sentiment_analyzer(comment['body'][:512])  # Truncate long texts
    print(f"Score: {comment['score']} | Sentiment: {result[0]['label']}")

Additional Information

Licensing Information

This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

You are free to:

• —Share and redistribute the material
• —Adapt, remix, and build upon the material

Under the following terms:

• —Attribution: You must give appropriate credit
• —NonCommercial: You may not use the material for commercial purposes

Citation Information

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

@misc{yingying_zhu_2025,
	author       = { YINGYING ZHU and XueRong Chen and Rafia Khayrullaeva },
	title        = { reddit-refractive-surgery (Revision 7982487) },
	year         = 2025,
	url          = { https://huggingface.co/datasets/Pandita-IA/reddit-refractive-surgery },
	doi          = { 10.57967/hf/6733 },
	publisher    = { Hugging Face }
}

Contributions

Dataset compiled and maintained by [Your Name/Research Group].

For questions, issues, or contributions, please contact: [yingying.zhu@alumnos.upm.es] or open an issue in the dataset repository.

Acknowledgements

We thank the Reddit communities r/lasik and r/LasikSupport for openly sharing their experiences and creating supportive spaces for LASIK patients. This dataset would not be possible without their contributions.

Ethical Considerations

This dataset is intended for research purposes only. Researchers using this dataset should:

• —Respect the privacy of community members
• —Avoid attempts to reidentify users
• —Consider the sensitive nature of medical information
• —Follow their institution's IRB guidelines when applicable
• —Comply with Reddit's API Terms of Use

Updates and Maintenance

Current version: 1.0 Last updated: [Date] Maintenance status: No regular updates planned

For the most current information, visit the dataset repository on Hugging Face.