Detect Review Manipulation by Leveraging Reviewer Historical Stylometrics in Amazon, Yelp, Facebook and Google Reviews

Abstract

Consumers now check reviews and recommendations before consuming any services or products. But traders try to shape reviews and ratings of their merchandise to gain more consumers. Seldom they attempt to manage their competitor's review and recommendation. These manipulations are hard to detect by standard lookup from an everyday consumer, but by thoroughly examining, customers can identify these manipulations. In this paper, we try to mimic how a specialist will look to detect review manipulation and came up with algorithms that are compatible with significant and well known online services. We provide a historical stylometry based methodology to detect review manipulations and supported that with results from Amazon, Yelp, Google, and Facebook.