Detect Coordinated Inauthentic Behavior on a News-Sharing Network
Overview
What this challenge is about.
You detect coordinated inauthentic behavior on a news-sharing network using temporal, content, and graph features to earn a verifiable certificate.
The scenario
The newsroom (around 20 staff, philanthropy + reader-funded) plans to publish CIB findings only if the methodology can survive a defamation lawyer's review, so the bar is evidence over algorithmic black-box.
The Brief
What you'll do, and what you'll demonstrate.
Detect and document coordinated inauthentic behavior in election-related posts with evidence robust enough for publication.
Earning criteria — what you'll demonstrate
- Combine temporal, content, and graph features for CIB detection
- Design evidence cards that hold up to editorial and legal review
- Validate detection against publicly disclosed ground truth
- Document methodology for a non-technical defamation-lawyer audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Network Analysis
Apply network analysis to solve real industry problems and demonstrate production-level capability.
- Anomaly Detection
Apply anomaly detection to solve real industry problems and demonstrate production-level capability.
- Near Duplicate Detection
Apply near duplicate detection to solve real industry problems and demonstrate production-level capability.
- Temporal Analysis
Apply temporal analysis to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Evidence Design
Apply evidence design to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Combining temporal, content, and graph features for a real investigative outcome is exactly the senior data-science work trust-and-safety teams hire for.
This challenge sharpens
- network-analysis
- anomaly-detection
- temporal-analysis
AI Safety Researcher
Documenting evidence chains for inauthentic-behavior detection sits at the intersection of trust-and-safety and AI safety research.
This challenge sharpens
- anomaly-detection
- evidence-design
- network-analysis
Applied AI Scientist
Translating detection methods into a defensible product (evidence cards) is the applied-AI scientist's daily work.
This challenge sharpens
- near-duplicate-detection
- evidence-design
- temporal-analysis