Google has released an explainer video that explains how Google handles reviews left for local businesses on Google Maps. The article describes the numerous steps and actions Google takes to review and publish user-generated reviews in a matter of seconds.<\/p>\n\n\n\n
Google revealed five steps it takes to ensure that Google Maps reviews are accurate and useful.<\/p>\n\n\n\n
A well-defined content policy serves as the foundation of Google’s approach to moderating reviews left on Google Maps.<\/p>\n\n\n\n
Every website that accepts user-generated content must have a clear policy outlining what is and is not acceptable. This assists users in understanding the limits and also informs moderators when to intervene.<\/p>\n\n\n\n
According to Google:<\/a><\/strong><\/p>\n\n\n\n \u201cWe\u2019ve created strict content policies to make sure reviews are based on real-world experiences and to keep irrelevant and offensive comments off of Google Business Profiles.\u201d<\/p><\/blockquote>\n\n\n\n Google’s content policy outlines the outcome they hope to achieve:<\/strong><\/p>\n\n\n\n \u201cContributions must be based on real experiences and information.\u201d<\/p><\/blockquote>\n\n\n\n Google’s content policy lists six types of prohibited activity.<\/p>\n\n\n\n Examples of Map Review Policy Violations in Review Content:<\/strong><\/p>\n\n\n\n The next step Google takes to protect the integrity of Google Maps Reviews is to incorporate the content policy into Google’s algorithms by using it as training data for the algorithm and its human moderators.<\/p>\n\n\n\n Google explains:<\/strong><\/p>\n\n\n\n \u201cOnce a policy is written, it\u2019s turned into training material \u2014 both for our operators and machine learning algorithms \u2014 to help our teams catch policy-violating content and ultimately keep Google reviews helpful and authentic.\u201d<\/p><\/blockquote>\n\n\n\n Google states that as soon as a review is posted, it is routed to their moderation systems for review.<\/p>\n\n\n\n Google employs both human and machine review systems. Within seconds, Google’s algorithms can process a review and approve it for publication.<\/p>\n\n\n\n Rather than relying on humans to complete tasks, Google has traditionally preferred to scale its systems with algorithms.<\/p>\n\n\n\n To determine whether a review is fake, the algorithm considers a variety of factors.<\/p>\n\n\n\n Among the review factors mentioned by Google are:<\/strong><\/p>\n\n\n\n Google explains the operation of its automated system:<\/strong><\/p>\n\n\n\n \u201cAs soon as someone posts a review, we send it to our moderation system to make sure the review doesn\u2019t violate any of our policies.<\/p> \u2026Given the volume of reviews we regularly receive, we\u2019ve found that we need both the nuanced understanding that humans offer and the scale that machines provide to help us moderate contributed content.\u201d<\/p><\/blockquote>\n\n\n\n Google has stated that it encourages businesses and the general public to report fake reviews.<\/p>\n\n\n\n This is a common way of moderating user-generated content (UGC).<\/p>\n\n\n\n This method is sometimes referred to as “Report-a-Post.” Report-a-Post is fantastic because it makes users feel like they are a part of a community and crowd-sources the review function, allowing users and businesses to apply their unique perspective to catch bad reviews that might slip past a moderator or an algorithm.<\/p>\n\n\n\n Google revealed an intriguing fact: it is proactive in anticipating events that could lead to abusive reviews. Google conducts increased monitoring of reviews of businesses in the vicinity of those events to ensure that only authentic and useful reviews are published.<\/p>\n\n\n\n Google shared:<\/strong><\/p>\n\n\n\n \u201cFor instance, when there\u2019s an upcoming event with a significant following \u2014 such as an election \u2014 we implement elevated protections to the places associated with the event and other nearby businesses that people might look for on Maps.\u201d<\/p><\/blockquote>\n\n\n\n Google’s approach to moderating user-generated content is consistent with a long-standing approach pioneered on forums and blogs, including the use of automated systems to deal with users and events that may lead to more abusive content.<\/p>\n\n\n\n This article is useful because the steps Google takes can be used as inspiration and a template for developing a strategy for moderating user-generated content on any website or platform that accepts user-generated content.<\/p>\n\n\n\n How reviews on Google Maps work<\/a><\/p>\n\n\n\nImportant information about Google Maps Review Content Policy<\/h2>\n\n\n\n
Step 2: Google’s Algorithm Is Integrated With Content Policy<\/h2>\n\n\n\n
Step 3: Google immediately moderates the reviews<\/h2>\n\n\n\n
Step 4: Google Promotes Community Moderating<\/h2>\n\n\n\n
Step 5: Google is proactive in detecting fake reviews<\/h2>\n\n\n\n
Google Maps Reviews Using Machine Learning and Human Moderation<\/h2>\n\n\n\n
Citations<\/h2>\n\n\n\n
Read Google\u2019s Blog Post<\/h3>\n\n\n\n
Read Google\u2019s Map Reviews Policy<\/h3>\n\n\n\n