Spam & Fake Reviews
Spam and fake reviews have become a ubiquitous problem for social media and ecommerce companies. Spammers and fraudsters can easily circumvent rules engines or supervised machine learning solutions with sophisticated attack techniques. DataVisor’s Unsupervised Machine Learning Engine provides the next level of defense against these sophisticated techniques by analyzing the hidden connections between accounts. It detects new and evolving attacks without training data or labels, and often before the spam or fake reviews have been sent.
How Spammers Evade Detection
Attackers simulate real user activity by uploading content scraped from other sites, fooling rules engines, supervised ML, and human reviewers.
Cyber-criminals employ armies of low-cost, on-demand human labor to bypass captchas and create realistic reviews and spam.
Fraudsters incubate fake accounts for months with simulated user activity to evade account-age and activity-based rules.
Fraudsters utilize proxies, VPNs or cloud-hosting services to hide their tracks from IP blacklists and rules-based systems.
Why UML Can Stop Sophisticated Attacks
DataVisor’s Unsupervised Machine Learning Engine is uniquely capable of stopping sophisticated, evolving attacks because it analyzes all accounts and events at once, uncovering the hidden connections between them. This approach allows DataVisor to detect spam or fake reviews without training data or labels, and even when individual accounts do not appear suspicious when viewed in isolation.
Detect Unknown Threats
Detect new and continually evolving attacks faster, without waiting for training data or labels.
Accuracy and Coverage
Detect malicious accounts at account registration before they can send spam or publish fake reviews.
Learn How DataVisor Stops Fake Reviews & Spam
Srigurunath Chakravarthi, Head of Trust at Pinterest discusses how DataVisor has helped detect more spammers faster with Unsupervised Machine Learning.
Watch this recording of a webinar hosted by Julian Wong, Technical Architect at DataVisor, and his special guest, Jim Blomo, Director of Engineering at Yelp, to learn how Yelp stops fake users while connecting people with local businesses.
The DataVisor Platform
Unsupervised Machine Learning Engine
Supervised Machine Learning Engine
Automated Rules Engine
Global Intelligence Network
What’s Happening with Spam & Fake Reviews
The DataVisor Online Fraud Report took a look at our base of more than one billion users across 172+ countries in the world. Using this massive amount of data, we were able to identify some of the favorite tools and attack techniques that online criminals from around the globe favor when doing their dirty work.
Social spam is not new. We’ve all experienced our share of unsolicited comments, stranger friend requests, phishing links, fake reviews, and click-baiting. But as online services introduce new features to make connecting and sharing
We are entering an era of billions of users and trillions of online accounts. This is attracting a growing wave of attacks like fake reviews targeting online services of all sizes. The Internet user population