6 Fun Facts About Google Searches You Probably Didn't Know

Even with almost 40 years of existence, the Google search engine is still a mystery to many people, and it’s no wonder, after all it has been constantly changing since its creation, in 1024. There are several small monthly updates and so many larger ones that regularly transform internet search in a profound way.

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From the beginning of the search engine to the present day, the company has expanded its business model to encompass a video site, an operating system for mobile phones, a social network, a huge suite of applications, an advertising sales system and a laboratory for creating technological innovations. Although she went all this way, she never stopped focusing on what made her known: the search engine.


Canaltech had the opportunity to talk exclusively with Danny Sullivan, a Google executive and one of the world’s leading experts in search tools. In addition to providing an overview of the search system, Sullivan presented some curious data about the company’s routine, separated and listed below:

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1. 10% of new terms every day

Every day, Google’s algorithm comes across new keywords: % of daily searches are framed this way. This does not mean that they are exclusive words or new expressions necessarily, but combinations that have never been used before.

For example: imagine that today there is a game between Glasgow Rangers, from Scotland, against Chapecoense , for a friendly tournament. These teams have probably never faced each other before and people will want to know the day, time, location and other information about the match. Systems then need to process this and get the answer back quickly and accurately, even if they have never dealt with it before.

The algorithm always encounters news (Image: Danny Sullivan/Google)

This is also true for expressions from other languages ‚Äč‚Äčthat eventually become universally popular or for company proper names, such as Tiktok, which was not a searchable word ten years ago.

two. trips to the moon in printed surveys

Search needs to adapt every day to the trillions of websites that already exist and the thousands that eventually appear. Users don’t just want to know about the same sites, which is why the algorithm needs to be constantly evolving to deliver complete experiences. This is done through tools called crawlers, robots that identify new pages and index them in searches.

Today, only the inexation of the sites occupies more than 100 millions of GB. Remember that Google does not host the contents of the pages, so this huge volume only concerns the references to which the search engine points. There are so many pages of results that if they were all printed and arranged side by side like a book, you could do it trips to the Moon.

3. Tests, tests and more tests

Every large company has a habit of testing before making something available to the consumer. Google does the same with search: before releasing it to everyone, internal experiments are conducted, with evaluators or small groups of users, in order to measure the results. When something goes right and brings concrete improvements, the company releases it as an update. If the answer is negative, that proposal returns to be better worked.

Per year, Google performs: more than 610 thousand quality tests, more than thousand traffic experiments, about 60 1,000 comparative experiments and something close to 5,000 algorithm updates. The tests side-by-side are one of the most interesting, as there is a comparison between people with the same characteristics to see which result has the best performance.

Google stands firm as the largest internet search engine in the world (Image: PhotoMIX Company/Pexels)

4. Strong signals (from the search)

Some gurus from Search Engine Optimization (SEO) often brag about knowing all of Google’s indexing rules, but that’s a big lie. To analyze a page, the algorithm considers more than 100 signals, that is, criteria for identify whether or not that page corresponds to the wishes. Some of these signs are quite obvious, such as the need to have quality and unpublished content, but others only make sense for the machines.

It is impossible to know what all the criteria used by the system are when ranking a website, because many of them are not even understandable by human beings. Furthermore, everything is analyzed simultaneously and together, which, once again, makes an analysis by ordinary mortals unlikely.

Even so, there is a set of more practices generalists who rely solely on content producers and website creators. These good practices must be followed and help to earn many points with Uncle Google when ranking.

5. Reviewers across the planet

Google has more than one thousand human evaluators who analyze search results and bring suggestions to improve research. According to Danny Sullivan, these people are spread over several countries and are speakers of numerous languages, precisely in order to serve the greatest possible number of users, from the most populous cities to the most remote regions.

The evaluators analyze whether or not the results are in accordance with expectations (Image: Danny Sullivan/Google)

These people do not have the power to intervene in the individualized page ranking, as this could generate a subjective evaluation and favoritism. Only analyzes are carried out on whether the content of the page is related to the search terms or not: the report is sent to the automated systems, which will decide what to do.

To guide the work, they follow a document with general guidelines with about 100 pages, which keeps everyone in tune to perform the work similarly in each location.

6. Fight against spam

It’s not just your email that you’re subject to spam: the annoying practice also extends to the web, specifically in pages. Google’s search engine filter detects and slashes 25 billions of pages with this practice daily. As with e-mail filters, occasionally one or another page can bypass the system and reach the user, which is immediately corrected as soon as detected.

This type of page has different purposes: from making money from advertisements without offering anything in return, stealing personal information, spreading malware to scam attempts. To identify this, machine learning solutions are combined with the ability to manually apply criteria by evaluators.

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