Review platforms and e-commerce websites are challenged with fabricated reviews, scam and spam and companies trying to game the system. From companies seeking to improve their profile with bought reviews, to using negative reviews to attack their competitors, maintaining trust and providing consumers with an authentic user experience is a major challenge.

89% of consumers globally check reviews online before making purchases

Source: Canvas8 2020

GUIDE: The Science of Fake Review Detection

Learn how AI-enabled technology is transforming the way online review platforms and e-commerce sites manage content and reviews to provide an authentic consumer experience.

Fabricated Reviews Explained

As the popularity of review sites grows, the sheer scale of data has made it increasingly difficult for platform owners to identify individuals and groups who may be trying to “game the system”, post inappropriate or fake content to harm competitors or boost sales.

Common areas for concern are:

  • Spam and scam
  • Fabricated reviews
  • Biased positive and negative reviews
  • Misbehaving companies trying to ‘game the system’

As user-generated review content increasingly forms a key resource for buyers, there is growing pressure from both legislators and consumers to remove fabricated reviews and provide a more authentic and useful consumer experience. Fundamentally this is about trust.

Our approach

The ability to have a wider view across large data sets is at the heart of our approach. This is where a combination of AI and graph technology can provide unique insights. Whether it is for tackling spam, scam, or fabricated reviews, we focus on looking for patterns of similar activity between individuals across your whole data set.

The result is the ability to remove persistent offenders and  fake content at scale; providing the authentic experience your users demand.  Find out more about our fake review detection software.

Common Phrases

Text that is short, full of common phrases, has broken English, or a low readability score becomes suspect

User Behaviour

What is the behaviour of the reviewer? Are they a one-timer or do they have a history? Does their activity seem genuine?

Review Patterns

What does the pattern look like for the company being reviewed? Have there been bursts of suspicious activity?

Looking to understand the scale of fabricated & biased reviews, scam & spam, and companies trying to ‘game the system’ ?

Request an Online Reviews Insight Report and we’ll show you the true scale of your challenge and how to address it.

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