It's not as easy as it seems. Many systems have already been devised, but fake reviewers adapt; it's an arms race, just like with email spam. Bing Liu, who has

It's not as easy as it seems. Many systems have already been devised, but fake reviewers adapt; it's an arms race, just like with email spam. Bing Liu, who has done work on sentiment analysis, has written about some techniques dealing with spam/fakes on review sites. Here's just a sampling of popular methods and flags: - Duplicate checking (same user on different products with similar reviews, for example) - Meta-reviews ("was this review helpful?" - can also be gamed) - user rating averages (all highs or all lows sometimes considered a flag) - ratio of "first product reviews" to total reviews on a per-product basis - "Super-reviewer" status (questionable, but often a flag) - Products with low sales ranks - Review ring detection (IP block, post times, etc.) - Early reviews - Users who give high ranks, while most other reviews are low - Positive reviews for one brand's products, and negative for others - "Verified" purchases A lot of these already have countermeasures, and fake reviews have already come up with counter-counter measures (like review-time staggering, using multiple IP blocks, shipping empty boxes to defeat verified purchases, etc.) The "how to know what's trustworthy?" is the million dollar question, and it has many answers that happen to change over time. Not easy to solve.