{"id":5418,"date":"2024-10-14T17:22:28","date_gmt":"2024-10-14T17:22:28","guid":{"rendered":"https:\/\/workhouse.sweetdishy.com\/?p=5418"},"modified":"2024-10-14T17:22:29","modified_gmt":"2024-10-14T17:22:29","slug":"major-algorithm-updates","status":"publish","type":"post","link":"https:\/\/workhouse.sweetdishy.com\/index.php\/2024\/10\/14\/major-algorithm-updates\/","title":{"rendered":"Major Algorithm Updates"},"content":{"rendered":"\n

Occasionally there is a major shift or technology applied to the algorithm. It may be an addition to the current algorithm or a replacement. These major algorithm shifts create dynamic changes in the rankings and are tracked by many SEO professionals. Evaluating these major updates over the years enables people to see where the trend is going and how the search engines, especially Google, prioritize the content of their results.<\/p>\n\n\n\n

As you’ll notice, the updates are named like hurricanes, as they mark major shifts in relevance, quality, and spam. Yes, websites built and optimized specifically to fool the search engines are called \u201cspam\u201d as well!<\/p>\n\n\n\n

Google Panda, 2011<\/strong>  One of the earliest and most impactful updates was the Panda update. For starters, this update targeted \u201cthin\u201d content that was very shallow or was duplicated across dozens or hundreds of pages. An example of \u201cthin content\u201d is a website attempting to rank for plumbing contractors and building a page for every city or town, then interlinking all the pages with the city name and the word plumbing<\/em> as the anchor text. The text on the page would be the same, with only the name of the city changing.<\/p>\n\n\n\n

This update also targeted websites that simply aggregated feeds from other websites and published them as their own. Other tactics were those that published duplicated or stolen content from other websites or had the same content repeated across the website (such as 200 how\u2010to articles on the same subject). Google targeted sites that were built specifically for rankings and had little to no quality based on user experience or the content itself. This was one of the first algorithms that focused on identifying the authorship of content, as many sites were simply stealing or appropriating content and publishing it as their own.<\/p>\n\n\n\n

Google Penguin, 2012<\/strong>  This update was targeted at sites developing false relevance from linking schemes. This targeted sites that were buying links or participating in link networks designed to inflate a backlink profile. Additionally, Google targeted websites that employed \u201cdoorway pages,\u201d which was a method of showing the search engine a false page designed for rankings but sending the searcher to another page.<\/p>\n\n\n\n

Google Hummingbird, 2013<\/strong>  Hummingbird was a unique update in that it focused on the on\u2010page content of websites rather than backlinks or spam. This algorithm was focused on a context\u2010driven evaluation of the content. This marked a major shift in Google’s search technology as the search phrase itself, rather than the individual words, was being evaluated for relevance. Previously, in a search for \u201cmechanical gaming keyboard\u201d the algorithm would attempt to find the best matches for the three words separately. This update was the first application to evaluate the intent of the phrase rather than the individual words.<\/p>\n\n\n\n

This update showed a dramatic shift in evaluating the content of pages, as simple keyword repetitiveness was not going to be effective. Rather, the natural use of synonyms, contextual cues, natural conversational content, and long\u2010tail keywords would be used to evaluate the page for relevance.<\/p>\n\n\n\n

For example, think of the many synonyms for cars: autos, automobiles, vehicle, motor, wheels, motorcar, hybrid, SUV, and so on. Webster’s lists 57 synonyms. In natural conversation, people use synonyms all the time, because using one word becomes repetitive and contrived. According to Google, \u201cSynonyms affect 70 percent of user searches across more than 100 languages.\u201d<\/p>\n\n\n\n

Google Pidgeon, 2014<\/strong>  Pidgeon was designed to enhance the local search results using distance and location. The emphasis started the development of enhancing the visibility of local businesses in the results.<\/p>\n\n\n\n

Google RankBrain, 2015<\/strong>  After these major algorithm shifts, there was a change, as many of the newer algorithms were additions to the primary algorithm. In this case, RankBrain was added as an update to the Hummingbird algorithm. RankBrain was a machine\u2010learning AI designed to learn words and concepts of rare or uncommon words. It was designed to understand the variations of different search phrases. In 2013, Google announced that 30 percent of search terms were phrases that had never been used before! Also, as new words, trends, and events happen around the world, Google needs to keep up with the increasing vocabulary. RankBrain was designed to learn these new words or phrases and connect them to meaningful results.<\/p>\n\n\n\n

Google Fred, 2017<\/strong>  Fred was another update that targeted false relevance, specifically keyword stuffing, link schemes, and misleading backlinks.<\/p>\n\n\n\n

Google BERT, 2019<\/strong>  While not an algorithm update, BERT (Bidirectional Encoder Representations) is a Google technology for natural language processing. The goal is to understand language nuance and idioms. As an example, think of the many ways that the word \u201clike\u201d is used in English:<\/p>\n\n\n\n