Design a Feature to Detect Fake Reviews

To design a fake review detection feature, I would first align on success metrics: maximizing the removal of fraudulent content while keeping false positives below 0.1%. My approach starts with data ingestion. We must verify if the reviewer has actually purchased the item via our order history; unverified purchases should carry lower weight or require additional scrutiny. Next, I'd analyze behavioral signals. Are multiple accounts posting similar text within minutes? Do they share device fingerprints or IP addresses? This suggests a bot network. I would also implement NLP models to detect unnatural language patterns, such as excessive superlatives or repetitive phrasing common in incentivized reviews. The system architecture would be a tiered funnel. Tier 1 applies hard rules (e.g., no review without a verified purchase) for immediate filtering. Tier 2 uses a machine learning classifier trained on historical flagged data to score suspicious reviews in real-time. Reviews scoring above a threshold go to a human moderation queue for final review before publishing. Finally, we need an appeal mechanism. If a user believes their genuine review was wrongly flagged, they can contest it, feeding back into our training loop to improve model accuracy over time.