Online sellers who generate fake feedback make near-perfect ratings less trustworthy than slightly lower scores. Shoppers normally assume that a higher average score always signals a better product or a more honest store. Low quality sellers aggressively manufacture top scores to reach the peak of the rating scale, which causes buyers to distrust flawless averages.
A merchant writes fake positive reviews with varying scores and posts them to shift the store’s public rating. This action works like a student mixing fabricated homework grades into a report card to mask poor exam results. When shopping platforms monitor and delete only the highest scores, sellers respond by posting fake reviews at slightly lower score tiers. The overall average rating stays the same, so buyers never see the shift in review distributions.
Researchers constructed a mathematical framework modeling low quality sellers, score-inspecting platforms, and buyers who evaluate displayed averages. The team separated the single visible average score from the hidden composition of individual reviews underneath it. The analysis showed that sellers facing valuable buyers can display lower scores yet achieve higher sales because moderate ratings retain credibility.
Platform designers can now build buyer-oriented ranking algorithms that measure what a rating conveys rather than relying on its raw numerical level. Platforms can evaluate the full mix of underlying reviews to prevent enforcement actions from simply shifting fake scores across the scale.