In the digital age of online reviews, many businesses rely on the opinions of satisfied customers to attract new business. But what happens when those reviews are fabricated or otherwise faked? Google is here to explain how they catch and act on fake local business reviews. To curb deception in the review landscape, Google has revealed its process for identifying fraudulent reviews, and Google is now taking action against such reviews. Google can flag and take down any questionable content using automated algorithms and manual efforts.
Contents
Google Steps In Against Fake Business Reviews
Google’s automated systems and human review teams have been busy removing harmful content from their platforms. According to a recent tech giant report, they removed over 200 million photos and 7 million videos that violated their policies in 2020 alone. This increased from 2021’s figures, which comprised 80 million photos and 10 million videos.
The report also stated that Google had been actively blocking or removing over 115 million reviews, another increase from last year’s figure of around 55 million reviews. These figures highlight the importance of Google’s efforts to ensure its platforms remain safe for users. It also shows how technology can work alongside human intervention to identify and act against harmful content online.
Google has reiterated its commitment to investing in advanced technologies, such as machine learning algorithms, to improve its automated detection capabilities.
How Does Google Catch Fake Local Business Reviews?
Google is using its machine learning models to analyze user-contributed reviews. These models aim to identify unusual patterns in the reviews that may indicate spam or fake content. For example:
A particular restaurant has an unusually high number of five-star reviews in a short period of time. This could be a red flag indicating that something suspicious is going on. Machine learning algorithms are trained to spot these anomalies and remove them from search results.
Another benefit of using machine learning models for review analysis is that they help identify abuse not seen before. This year, Google’s automated systems noticed a sharp increase in business profiles with webpages with the “.design” and “.top” extensions. Further analysis by the company’s security analysts revealed that these profiles were fake and likely part of a scamming operation. The creation of fraudulent profiles was to deceive users and possibly steal their sensitive information.
Conclusion
There has been a rise in fake or fraudulent content submitted to Google’s Maps system.
To combat this, Google has now updated its systems with new measures to ensure posting of only genuine content. The search engine giant reviews all new content before publishing it on Google Maps, which helps ensure that it meets its strict criteria for quality and authenticity. In addition, Google’s security team is now more actively monitoring and removing fraudulent or spam content from the Maps platform.