Fake reviews are emerging as one of the most significant factors eroding consumer trust in online marketplaces. In 2021, fake reviews influenced US $152bn of e-commerce spending worldwide. Bad actors understand the power of reviews and look to manipulate, incentivize and pay for fake reviews to boost their rating or undermine their competitors - with gains far outweighing the risks of being caught.
Breaking down the results of our recent survey to its key findings, the statistics paint a clear picture:
- 92% of surveyed consumers are concerned about fake reviews
- 75% of respondents want fake reviews found and flagged rather than automatically removed
- Almost half of respondents believe it is the joint responsibility of both the platform or marketplace and business being reviewed
The UK, EU and US are currently at various stages of progress in legislating against fake reviews. However, irrespective of legislation, our survey shows consumers want to be able to trust that the reviews they read are real and know when they are not. Transparency is key to gaining and retaining consumer trust.
Here’s how Pasabi can help:
Continual monitoring to understand your reviews activity
Pasabi uses continual monitoring to analyze reviews on your platform and recognise suspicious activity as it appears.
Use AI technology to automate against fake reviews at scale
As you scale, it’s impossible to manually check every review on your platform. Thankfully, AI technology is designed to handle volume very efficiently. Our fake review detection technology uses AI to help our customers eliminate reviews abuse by detecting fake reviews, and the fake accounts behind them, on their platform. The valuable insights we provide to investigation teams means platforms can increasingly automate their enforcement actions with a high level of verified accuracy.
Focus on behaviors not just review content
Fake reviews, recommendations, page likes, thumbs up, emojis and hearts are all examples of how reviews can be manipulated for improved ratings and, ultimately, financial gain. It’s not possible to identify a fake review with certainty based on the text alone. And with AI tools producing very credible-sounding content this is getting increasingly harder to detect.
Our technology analyzes behavioral risk signals in your data to detect many types of suspicious review activity such as:
- gated reviews - holding back the negative reviews
- incentivised reviews - discounts, free gifts, reimbursements etc.
- positively biased - overly positive, ‘too good to be true’ reviews
- negatively biased - overly negative often used to attack competitors
- paid-for reviews - purchased from review selling companies
Connect suspicious behaviors to find review sellers
Review manipulation services and organized review seller groups on social media are a thriving industry. Focusing on behaviors, Pasabi’s graph technology draws links between individuals and groups of individuals by analyzing thousands of data points. Our platform filters out the unconnected posts and unrelated businesses, leaving a smaller data set for more detailed analysis.
Our cluster detection technology reveals patterns in the data that we know, from working closely with our customers and training our algorithms, identify review seller activity and when groups are working together.
Prioritize action against the worst offenders
Our approach finds the worst offenders, giving enforcement teams the evidence they need to decide where they should take action to have the strongest impact.
As our survey has shown, fake reviews are a growing threat in the minds of consumers. Addressing the challenge, and being more transparent with customers, will improve their trust in your platform and boost your reputation.
If you would like to learn more about how Pasabi can help tackle fake reviews, why not book a demo.
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