Internet search and filtering quality

Internet search and filtering quality
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There are various search engines on the Internet that search the entire Internet, or just individual databases.

Search engines that search the entire Internet are based on keyword searches.

This search is based on a constant automated search of all pages that can be accessed without a password, and counting the words that appear on that page. This data is entered into a large indexed database that contains all the words that someone has typed somewhere, and links to the pages where that word appears. Each link also records how many times the word appears on that page, and how many visitors each page has. From this, the relevance of an individual page is calculated.

Search engines can increase this relevance to those sites that pay for advertising, those that are located in a certain territory, or according to some other specific criteria. These browsers can also recognize the type of data such as texts, images, movies, folders, news, books, dates, language and the like.

Some social networks have their own search engine filters that search only their databases. These search engines are also based on keywords, but have other additional criteria by which their database can be searched.

These criteria are for example; time when a text, image or film is posted on a social network, type of data posted on the network (text, image, short film, long film, live broadcast, shows, playlists, etc.), duration, technical characteristics of the data , and can also be sorted most often by relevance, number of likes, rating, number of views, or date of posting.

In this way, all results agree most often on relevance, likes and ratings of other viewers.

The quality of the search in this way of searching and sorting mostly depends on the relevance, ratings and number of views, and the most important quality criteria are likes, and the number of views and comments.

The most frequent visitors to social networks are children, who most often rate the content, give likes and share the content, which increases the number of views. That way, the most popular content is funny content or shocking content. In addition, some content authors pay to advertise their content, likes and share so that as many people as possible see their content.

The vast majority of information authors do this out of fun, or boredom, and the quality of their work on social media is usually nonexistent. Based on likes and other activities, advertising agencies build a user profile and send him advertising content based on that.

Quality content is hidden in a huge amount of junk or advertising data.

Thanks to this, the probability that viewers will find out the best information about what interests them is very small.

Some social networks have tried to improve the relevance of the search so that the authors of the content themselves define their content according to different thematic criteria. However, the vast majority of authors do not do this, due to ignorance, or because individual contents can be classified into several different topics at the same time, so they do not know where to classify them.

The relevance of the search could be improved if the quality of the content was assessed on the basis of data on those who positively liked or shared some content. In this way, profiles of content could be created on the basis of varnishes, and not viewers. It is not the same if a text about a machine is evaluated by a child or an adult, a poet or an engineer.

For this assessment of the relevance of the content, anyone who wants to give someone a like, or share the content would have to register, and should enter their date of birth, gender, and their professional qualifications and profession. Based on this registration, the search could be searched by the number of likes that some content received from members of a certain profession, or a certain age, and not from everyone who likes some content.

Young people could ask for what was positively assessed by young people, while experts would only ask for what was positively assessed by members of their profession. In such a search, the filter search engine could offer individuals content that is in the user's profession, but the user could change the search criteria himself, looking only for results that are best rated by some age, profession or gender.

 

Other of my technical analyzes and innovations can be found in this book.