IndexFair › Methodology › Review filtering
/methodology · v2.1 · append-only · last updated 2026-01
/methodology/review-filteringRelated
Review filtering
A deterministic filter removes signals that can't be scored or that look manipulated, before anything counts toward a number.
What it means
The filter applies four deterministic checks to every review before it is eligible to contribute to a score. Each check has a published category name and a published count of what it removed — the exact thresholds and detection logic are withheld so a bad actor cannot tune around the filter.
Inputs & data fields
FieldTypeMeaning
content_fingerprinthashContent-derived hash used to collapse exact and near-duplicate reviews.
posted_attimestampPosting time; used to detect coordinated burst patterns.
content_quality_signaljsonbPre-computed signals: length_chars, paragraph_count, sentiment extremity.
aspect_ratingsarrayExtracted aspect ratings; review dropped if none are gradeable.
Process & formula
Categories we disclose
01De-duplicate by content fingerprint
02Drop posting bursts & coordinated timing
03Length & sentiment anomalies
04No gradeable aspect detected
Withheld by design. We publish the categories and the counts of what is set aside — never the exact thresholds or weights, which would let a bad actor tune around the filter.
How it affects public pages
Example: 5,132 collected → 3,324 used (65%). Affects the funnel on /review-signals.
Worked example
Lumen Exchange · fake data
01Collected: 8,214 reviews
02De-duplicated: −412
03Burst / anomaly removed: −1,630
04No gradeable aspect: −540
→Used: 5,632 (68%)