Can AI detect fake reviews on retail websites?

A Persistent Problem for Online Retail Trust

Fake reviews — whether posted by bots, paid reviewers, or coordinated networks trying to inflate or damage a product’s reputation — undermine one of the core trust mechanisms that online shoppers rely on when making purchase decisions. Because reviews carry real influence over buying behavior, they’ve also become a target for manipulation, prompting retailers and review platforms to invest in AI-based detection systems aimed at identifying and removing fraudulent reviews before they mislead shoppers.

This is very much an ongoing effort rather than a solved problem, since detection techniques and manipulation tactics continue to evolve in response to each other.

How Detection Systems Actually Identify Suspicious Reviews

AI-based fake review detection typically analyzes multiple layers of signals rather than relying on any single indicator. Behavioral signals include things like how old a reviewer’s account is, whether they have a verified purchase associated with the review, how frequently they post reviews, and whether their reviewing pattern matches typical genuine customer behavior. Timing patterns are also significant — a sudden burst of similar reviews appearing within a short window, especially around a product launch or after negative publicity, is a common red flag that can indicate coordinated, inauthentic activity.

Language-based analysis complements these behavioral signals by examining the review text itself for characteristics associated with fake reviews, such as unnaturally generic phrasing, repetitive patterns across multiple reviews attributed to different accounts, or language resembling known templates used in review manipulation schemes.

The Ongoing Challenge and Growing Regulatory Attention

Detecting fake reviews remains genuinely difficult because those generating fraudulent reviews actively adapt their tactics as detection methods improve, creating an ongoing back-and-forth rather than a permanent fix. No detection system can guarantee catching every fraudulent review, and there’s also a risk of occasionally misclassifying legitimate, unusually worded reviews as suspicious. This has led some regulators to get more directly involved, treating fake or deceptively incentivized reviews as a form of unfair or deceptive trade practice subject to consumer protection enforcement, adding a legal dimension to what was previously handled purely as a platform trust-and-safety issue.

Given these ongoing challenges, most review platforms and retailers treat fake review detection as a continuous investment area, regularly updating their models and enforcement practices rather than treating any single detection system as a permanent solution.

Bottom Line

AI can detect many fake reviews by analyzing behavioral patterns, suspicious timing, and unnatural language, but this remains an ongoing challenge as manipulation tactics continue to evolve alongside detection methods. Regulators in some jurisdictions have also begun directly addressing fake reviews as a consumer protection issue, adding legal consequences to what was previously handled mainly as a platform enforcement matter.

Go deeper

• Should Businesses Disclose When a Product Review Was AI-Generated?
• Can AI Also Be Used to Detect and Fight Misinformation?
• Why Is AI-Generated Misinformation Harder to Detect Than Traditional Fake News?