Multimodal Emotional Distress Detection on Reddit

Project Overview

This research project develops an AI-driven system to detect emotional distress signals on Reddit using a multimodal approach (text, images, and user engagement patterns). By analyzing posts from mental health-focused subreddits (e.g., r/TrueOffMyChest), the system identifies early signs of distress through:

• NLP-based sentiment analysis (BERT/RoBERTa)
• Computer vision for facial emotion detection in images/GIFs
• Behavioral tracking (post frequency, engagement metrics)

Key Objectives

1. Real-time monitoring of Reddit content
2. Multimodal fusion of text and visual data
3. Ethical AI implementation with privacy safeguards

---

Dataset

The dataset contains 10,000 Reddit posts

• Access: GitHub Dataset Repository
• Composition:
  • Text posts (title, selftext)
  • Image URLs (thumbnails, uploads)
  • Metadata (subreddit, created_utc, score, num_comments)
  • Flair tags (link_flair_text, author_flair_text)

---

Installation

1. Clone the repository:

   git clone https://github.com/wk23aau/distress-detector.git  
   cd distress-detector
   

2. Set up Python environment :

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate     # Windows

3. Install dependencies :

   pip install -r requirements.txt 

4. Configure API keys : Create a .env file with:

  REDDIT_CLIENT_ID=your_client_id  
  REDDIT_CLIENT_SECRET=your_client_secret  
  REDDIT_USERNAME=your_username  
  REDDIT_PASSWORD=your_password  
  GITHUB_TOKEN=your_github_token

---

Usage

1. Data Collection :

python reddit_scraper.py --subreddit TrueOffMyChest --limit 10000 --batch-size 100

• Scrapes posts in batches (default: 100 posts/batch)
• Stores cleaned JSON data in data/raw/reddit-posts/

2. Text Analysis

from analysis.text_analysis import analyze_sentiment  
analyze_sentiment("data/raw/reddit-posts/TrueOffMyChest_1-1000_2024-01-20.json")  

• Uses BERT for sentiment classification
• Extracts distress keywords and phrases

3. Image Analysis

from analysis.image_analysis import detect_emotions_in_images  
detect_emotions_in_images("data/raw/reddit-posts/TrueOffMyChest_1-1000_2024-01-20.json")  

• Leverages OpenCV and DeepFace for facial emotion detection

---

Project Structure

distress-detector/  
├── data/  
│   ├── raw/            # Raw scraped Reddit JSON files  
│   └── processed/      # Cleaned and analyzed data  
├── src/  
│   ├── reddit_scraper.py   # Data collection script  
│   ├── text_analysis.py    # NLP and sentiment analysis  
│   └── image_analysis.py   # Computer vision pipeline  
├── models/  
│   ├── bert_model/     # Fine-tuned NLP model  
│   └── cv_model/       # Trained computer vision model  
├── notebooks/          # Jupyter notebooks for experimentation  
└── requirements.txt    # Python dependencies  

Ethical Considerations

Anonymization : All user identifiers (e.g., author) are removed during preprocessing Compliance : Adheres to Reddit's API terms and GDPR/CCPA guidelines Transparency : Model decisions are logged with explainability reports

Contributing

• Fork the repository
• Create a feature branch (git checkout -b feature/YourFeature)
• Commit changes (git commit -m "Add feature")
• Push to GitHub (git push origin feature/YourFeature)
• Submit a pull request

License

This project is licensed under the MIT License . See LICENSE for details.

Contact

• Research Lead : Amna Zafar ([email protected] )
• GitHub Issues : Project Issues