Unveiling Google’s Tactics Against Fictitious Local Business Reviews

2 min read

In a blog post, Google announced that they have updated their machine learning systems to detect and eliminate more fraudulently contributed photos and videos, fake business listings, and fake reviews.

The automated systems and human review teams blocked or removed over 200 million images, 7 million videos, and over 115 million reviews. This is a 20% increase over the previous year, 2021.


Google’s Approach to Detecting User-Contributed Spam


Google has integrated novel machine learning models to identify and eradicate counterfeit and deceitful content.

These sophisticated models meticulously analyze user-contributed content, identifying irregularities and flagging emerging forms of abuse that were previously undetected.


Google explained:

“We rely on machine intelligence to pinpoint potential abuse patterns, consistently advancing our technology.

In the past year, we significantly upgraded our machine learning models, enabling us to swiftly recognize innovative abuse trends multiple times faster than in previous years.

For instance, our automated systems rapidly identified a surge in Business Profiles linked to websites, concluding with .design or .top — a challenge to detect across millions of profiles manually.

Our analysts promptly confirmed the falsity of these websites, allowing us to remove them and deactivate associated accounts expeditiously.”

Google’s systems thoroughly review new content before posting to prevent the submission of fake or deceitful content into the Google Maps system.

Furthermore, they utilize a machine learning model to scrutinize already published content to identify and intercept fraudulent content that might have evaded the initial reviews.

These newly implemented systems exhibit swifter spam blocking compared to their performance in 2021, demonstrating an enhanced capacity to capture fraudulent content.


Google elaborated:

“In certain instances, scammers attempted to superimpose inaccurate phone numbers onto contributed photos, seeking to deceive unsuspecting individuals into contacting the scammer instead of the genuine business.

We introduced a novel machine-learning model that identifies overlaid numbers on contributed images by analyzing specific visual attributes and photo layouts to address this concern.

Through this model, we effectively identified and prevented the publication of most of these fraudulent and policy-violating images.”


In 2022, Google made significant strides in combating spam:

  • Over 115 million reviews were blocked or removed, most intercepted before publication.
  • New anti-spam algorithms eliminated over 200 million photos and over 7 million videos that breached Google’s content policies.
  • Approximately 20 million attempts to generate fake business profiles were thwarted.
  • Enhanced safeguards were extended to more than 185,000 businesses encountering suspicious activities.

Furthermore, in January 2023, Google communicated to the FTC (accessible in the provided PDF) that it relies on various signals to identify fraudulent accounts and supplement content reviews.

Additionally, Google introduced image scanning capabilities to detect content overlayed onto images, mainly intended to redirect phone calls away from legitimate businesses toward scammers’ phone numbers.

They conduct checks for bots, identify duplicate content, and scrutinize word patterns resembling known fake reviews. Additionally, they employ a system called “intelligent text matching,” aiding in identifying deceptive or misleading content.


Upholding Trust: Google’s Commitment to Authenticity and Safety on Google Maps


Google ensures authenticity, safety, and reliability across the Google Maps ecosystem by leveraging a blend of automated systems and human reviewers.

The vigilance in detecting and thwarting fraudulent activities on Google Maps remains pivotal, serving users’ interests dependent on business reviews and the businesses listed within the system.

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Shilpi Mathur
[email protected]