Skip to content

Research Methodology

Overview of the research methodology and analytical approach.

Research Data Collection

  • Ethical collection of publicly available Reddit content
  • Cross-platform community analysis across immigrant communities
  • Multilingual data gathering for diverse populations
  • Anonymization and privacy protection protocols

Network Analysis Methodology

Our research methodology includes sophisticated network analysis to understand community resilience patterns:

Research Network Construction

  • Social interaction mapping from comment reply structures
  • Analysis of engagement and response patterns
  • Community clustering to identify knowledge brokers
  • Temporal analysis of information flow dynamics

Research Network Analysis

Our research generates network visualizations that reveal: - Information propagation patterns - How health information spreads through communities - Community response mechanisms - How communities address and correct misinformation - Knowledge broker identification - Influential users in health discussions - Support network structures - Peer support patterns in health contexts

Case Study Methodology

Four key research case studies include network analysis: 1. U=U Information Spread - Research on how accurate treatment information circulates 2. Subtle Misinformation Detection - Study of challenges in identifying partially false information 3. Peer Support Networks - Analysis of community response to personal health announcements 4. Knowledge Gap Analysis - Research into areas where community education is needed

Each network analysis reveals patterns of node connectivity based on community engagement and information flow structures.

Research Analysis Framework

  • Misinformation detection through community-centered approaches
  • Community resilience metric development
  • Cross-cultural health communication patterns
  • Validation through human expert annotation

Technical Implementation

The research methodology is implemented using: - Python-based analysis pipeline - PostgreSQL database with pgvector for semantic analysis - NetworkX for graph construction and analysis - Scientific computing libraries for statistical analysis