Crisis Network Analysis

Multi-layer social network analysis platform for crisis events — detecting community structure, information flow, and behavioral patterns at scale.

What It Does

• Builds 4 simultaneous network layers (user interactions, content flow, temporal evolution, semantic similarity) from Reddit crisis data
• Detects community structure using Louvain community detection
• Scores content influence with the novel Resonance+ metric — combining novelty, persistence, crisis relevance, and linguistic signals
• Classifies influential nodes into 6 hub types: Structural Hubs, Information Brokers, Resonance Leaders, Cognitive Influencers, Crisis Specialists, and Community Coordinators
• Runs LIWC linguistic analysis across crisis timelines to surface cognitive and emotional shifts in community language
• Delivers results through an interactive Streamlit dashboard with network visualizations, temporal charts, and exportable reports

Crisis Events Analyzed

Event	Period	Scale
LA Wildfires	Jan 7–31, 2025	30+ deaths · 200k+ evacuations · r/LosAngeles, r/wildfire
Turkey-Syria Earthquake	Feb 6–20, 2023	50k+ fatalities · international response · r/Turkey, r/syria

Tech Stack

Layer	Libraries
Network Analysis	NetworkX 3.5, python-louvain
Data	Pandas 2.3, NumPy 2.3, PRAW (Reddit API)
NLP / Linguistics	LIWC, scikit-learn
Dashboard	Streamlit, Plotly
Infrastructure	Docker, pytest

Key Features

Multi-Layer Networks

• User Network — reply/mention interaction graph
• Content Network — information flow and repost cascades
• Temporal Network — time-sliced evolution of discussion
• Semantic Network — keyword and topic co-occurrence

Resonance+ Metric Scores each post across four weighted dimensions: in-conversation novelty, local persistence, crisis domain relevance, and LIWC-derived cognitive resonance. Surfaces high-impact content beyond simple engagement counts.

Hub Classification Louvain community detection + centrality analysis classifies users into 6 functional roles, enabling targeted analysis of how information gatekeepers behave across different crisis phases.

Quick Start

git clone https://github.com/yourusername/crisis-network-analysis.git
cd crisis-network-analysis

python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

cp config/api_keys.env.template config/api_keys.env
# Add your Reddit API credentials to config/api_keys.env

streamlit run dashboard/app.py        # Launch dashboard
python scripts/test_setup.py          # Verify installation

Docker:

docker build -t crisis-network-analysis .
docker run -p 8501:8501 -v $(pwd)/data:/app/data crisis-network-analysis

Architecture

src/
├── collection/       # Reddit API data collection
├── preprocessing/    # Cleaning, deduplication, quality scoring
├── networks/         # Multi-layer network construction & metrics
├── social_computing/ # Resonance+ calculator, LIWC processor
├── visualization/    # Plot generation
└── utils/            # Config, logging

dashboard/            # Streamlit app (6 analysis pages)
config/               # Crisis event definitions, analysis params
data/                 # raw/ · processed/ · networks/ · results/

Contributing

pip install -r requirements-dev.txt
pre-commit install
pytest tests/                         # Run test suite
black src/ tests/ && isort src/ tests/

1. Fork → feature branch → PR
2. All PRs require passing tests and black/isort formatting