Counterforce-One
Community Resilience to Health Misinformation among Gay, Bisexual, and Other Men who have Sex with Men (gbMSM) Using Reddit
A computational machine learning and network analysis project, powered by Counterforce technology, analyzing how online communities respond to health misinformation in LGBTQ+ digital spaces.
Featured Case Studies
Four carefully annotated posts demonstrating different types of health information challenges
Key Findings
Preliminary insights from analyzing 11,000+ comments across health discussions
Communities Self-Correct Effectively
Even posts with 2,000+ upvotes receive constructive criticism. The "U=U" post shows accurate science reinforced by community adding context about trust and adherence.
Subtle Misinformation is Challenging
The "HIV is life altering" post contains real science with legitimate citations, but alarmist framing suggests treatment failure.
Peer Support Networks Are Powerful
30+ year HIV survivors mentor newly diagnosed individuals, combining practical advice with emotional support.
Knowledge Gaps Persist
PrEP/condoms discussion reveals some don't know PrEP only prevents HIV. Community self-educates about drug-resistant gonorrhea.
Methodology
A multi-stage research pipeline combining automated analysis with human expertise
Data Collection
- •Reddit API (PRAW) for public health discussions
- •PostgreSQL with pgvector for semantic search
- •Multilingual content support (4 languages)
- •Time period: 2024-2025
Classification
- •LGBTQ+ content classifier
- •Health misinformation scorer
- •Language detection
- •Vector embeddings for similarity
Network Analysis
- •User interaction graphs
- •Community detection algorithms
- •Centrality metrics for knowledge brokers
- •Reply relationship mapping
Human Annotation
- •Ground truth dataset (4 key posts)
- •Accuracy labeling
- •Severity assessment
- •Community response coding
Technologies Used
Research Roadmap
Clear milestones for expanding this research
Short Term (2-4 Weeks)
- Annotate 20-30 posts for robust ground truth
- Train ML classifier on annotated data
- Expand network analysis to all 30 top posts
- Calculate inter-rater reliability
Medium Term (1-2 Months)
- Temporal analysis: how does misinformation spread over time?
- Knowledge broker analysis: identify key educators at scale
- Intervention design recommendations
- Cross-community comparison
Long Term (3-6 Months)
- Cross-platform comparison (Reddit vs other social media)
- Automated early warning system for misinformation
- Partnership with public health organizations
- Academic publication and conference presentations