Text as Data: Social Network Analysis & Natural Language Processing

Graves’ Disease Reddit Community Exploration

📌 Project Overview

This academic project explores the online interactions and shared experiences of users in the Reddit community focused on Graves’ disease — an autoimmune thyroid condition. Using Social Network Analysis (SNA) and Natural Language Processing (NLP) techniques, the study uncovers communication patterns, influential users, key discussion topics, and sentiment trends within the community.

🗂 Full project report: 📄 Text As Data.pdf

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🧰 Tools & Technologies

Category	Tools / Libraries
Data Collection	Reddit API, PRAW
SNA & Graphs	NetworkX, iGraph, Gephi
NLP & Text Mining	spaCy, NLTK, Scikit-learn, Gensim
Visualization	Matplotlib, WordCloud, custom butterfly-shaped plots
Topic Modeling	TF-IDF, LDA, Coherence Score, Perplexity
Sentiment Analysis	VADER

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🧠 Objectives

• Detect influential users and communication flows within the Reddit community.
• Identify central users and community structures using SNA metrics.
• Analyze emotional tone and shared concerns using NLP techniques.
• Surface common keywords and latent discussion topics.
• Visualize sentiment-driven word clouds using a custom butterfly (Thyroid) shape.

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🔗 Main Components

🔹 1. Data Collection & Preparation

• Fetched top 30 posts and all comments from /r/gravesdisease using the Reddit API.
• Saved as structured CSV files for posts, comments, nodes, and edges.
• Labeled users as posters or commenters, and constructed interaction graphs.

🔹 2. Social Network Analysis

• Built directed graphs using NetworkX & iGraph.
• Computed:
  • Degree Centrality
  • Betweenness Centrality
  • Harmonic Closeness Centrality
• Detected cut vertices and critical bridges in communication.
• Identified strong & weak connected components.

🖼 Initial Network visualization by Gephi

🖼 Modularity Maximization Tracking

🔹 3. Community Detection

• Used the Girvan-Newman algorithm to detect communities.
• Visualized results with circular layouts and modularity tracking.
• Interpreted central vs. peripheral communities, inter-community ties, and influence hubs.

🖼 Before Labeling Communities

🖼 Community Layout (Circular Structure)

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🔹 4. Natural Language Processing

Text Preprocessing

• Emoji-to-text, lowercasing, regex cleaning, stopword removal, lemmatization.
• Used spaCy, NLTK, and regex for fine-grained cleaning.

Sentiment Analysis

• Used VADER to assign polarity scores to each post/comment.
• Classified as: Positive, Negative, or Neutral.

Keyword Extraction

• Extracted most frequent terms using CountVectorizer.

Topic Modeling

• Performed LDA with TF-IDF to uncover dominant themes.
• Selected optimal number of topics using Coherence Score and Perplexity.

🖼 Topic Modeling Evaluation - Coherence vs. Perplexity

🔹 5. Word Cloud Visualization

• Designed a custom butterfly-shaped mask to generate three word clouds:
  • 🟢 Right wing → Positive sentiment
  • 🔴 Left wing → Negative sentiment
  • 🔵 Body → Neutral sentiment

🖼 Sentiment Word Cloud (Butterfly Mask)

This project demonstrates the combination of social graph theory, natural language processing, and visual storytelling to explore real-world online health communities.