Google publishes a report that shares how it catches fake business reviews and profile.
Google published a blog post that shared that they updated their machine learning systems in order to catch and remove more fake reviews, fake business listings and fraudulent contributed images and videos.
The automated systems and human review teams removed over 200 million photos, 7 million videos and blocked or removed over 115 million reviews, which represents a 20% increase over the prior year, 2021.
How Google Catches User Contributed Spam
Google is using brand new machine learning models to catch and remove fake and fraudulent content.
These machine learning models look for unusual patterns in user contributed content, including flagging new forms of abuse that hadn’t previously been seen.
Google’s systems review new content before it is posted in order to block fake or fraudulent content submitted to the Google Maps system.
They also deploy a machine learning model to scan content that is already published, to catch fake content that may have slipped through the initial reviews.
These new systems block spam faster than in 2021 and catch more of it.
