Our Data Journalism Methodology
CarCodeFix is a data journalism platform that aggregates and analyzes real owner experiences from automotive communities. Here's how we transform thousands of discussions into actionable repair insights.
Data Collection
We scan 50+ automotive communities including Reddit, dedicated forums, and YouTube
Data Extraction
Pattern matching identifies OBD codes, parts, costs, mileage, and symptoms
Clustering
Similar issues are grouped by vehicle, problem type, and solution approach
Analysis
We synthesize findings into comprehensive repair guides with statistics
🌐Data Sources
- •Reddit automotive communities (r/MechanicAdvice, r/Cartalk, brand-specific subs)
- •Dedicated automotive forums (model-specific communities)
- •YouTube repair video comments and tutorials
- •NHTSA complaint database for safety-related issues
🔤Text Analysis Pipeline
- •OBD code extraction using pattern matching (P0xxx, B0xxx, C0xxx, U0xxx)
- •Cost extraction from mentions like "$150", "$1,200" with context analysis
- •Mileage parsing from "85k miles", "100,000 miles" formats
- •Part and symptom identification using automotive vocabulary patterns
- •Sentiment analysis to gauge owner satisfaction with repairs
✅Solution Verification
- •[SOLVED] tag detection in post titles and updates
- •Follow-up analysis: "fixed it", "that worked", "problem solved"
- •Community validation through upvotes and reply patterns
- •Cross-referencing solutions across multiple sources
📈Statistical Analysis
- •Repair cost distribution with min/max/average calculations
- •Mileage occurrence patterns (when problems typically appear)
- •Success rate analysis for different repair approaches
- •DIY vs professional repair tracking
- •Trend analysis over time (increasing/decreasing problem frequency)
🛡️Quality Assurance
- •Minimum data threshold: 5+ owner reports before publishing
- •Source diversity requirement: data from multiple communities
- •Spam and bot filtering using automated detection
- •Confidence scoring for extracted information
- •Regular updates when new data becomes available
Our Data at a Glance
Transparency Commitment
We believe in full transparency about our process. Our content is created by analyzing real owner discussions - we don't fabricate data or invent solutions. Every statistic can be traced back to actual community discussions. When we don't have enough data for confident analysis, we clearly indicate this with a "Preliminary Data" label.
Human Expert Review
While our data pipeline is automated, high-traffic articles undergo review by certified mechanics and automotive professionals. We partner with ASE-certified technicians through Upwork to verify technical accuracy of our most-viewed content.