Having trouble with your visibility in Google?
Rely on the specialists from KS!
Check out the offer!
Internet search engines
The popular Google search engine (as well as others such as Bing) combines the crawling and scraping process to index websites. Thanks to the collected data, Google is able to display to the user in a fraction of a second a ranking of pages that should fulfill the purpose of their query.
The origin of data obtained through web scraping methods can be proven
Interestingly, illegal use of data obtained through web scraping methods can be proven. Google scraped song lyrics from the Genius platform, the largest website with song lyrics in the world. Why does Google need song lyrics? At one time, song lyrics appeared at the very top of search results in the search engine when the user entered the appropriate query. At that time, the displayed lyrics were not marked with information that they came from the Genius service. The Genius service, suspicious of Google's activities, began marking its texts in such a way that it used apostrophes with a different Unicode code. The characters were swapped in precisely defined positions. The second, fifth, thirteenth and other apostrophes were swapped, which clearly indicated that the text displayed by the Google search engine came from the Genius service.
Machine learning, data science and web scraping
Web scraping has a number of advantages, including the speed of data acquisition, which allows for filling huge databases in a short time. This has not escaped researchers, technology enthusiasts and companies that are eager to acquire data, then used to train their neural networks . Sometimes there is a need to obtain external data when we do not have the appropriate amount of it ourselves. Of course, we do not have control over external data and it must be properly cleaned and prepared before analysis. Data cleaning itself usually takes more time than machine learning. Machine learning researchers relatively often resort to obtaining data using this method.
An example would be developing a model that aims to determine whether an opinion on a given topic or product is positive, neutral or negative. Such a model can be successfully used in marketing zalo database automation . To create such a model, it is necessary to collect a large (in the order of a million or millions) number of opinions from various websites along with an assigned rating, e.g. on a scale of 1 to 5. The collected data, after adapting them to the machine learning process, will allow the creation of an effective tool that will allow for the selection of negative comments, thanks to which the person responsible for the company's PR will be able to react faster in the matter of a damaged brand reputation.
How is web scraping different from web crawling?
What if we know what data we want to obtain, but we don't know all the URLs where the data is located? Both web scrapers and web crawlers are considered internet bots, but there is a conceptual difference between them. A typical web scraper has a ready list of URLs from which it will extract data before it starts working . Such a list can be prepared manually or a web crawler can be used.
A web crawler to collect links basically needs the first URL address from which it will start searching for more links. Depending on the configuration, the web crawler collects more addresses, which it then adds to its internal queue of links to visit. The web crawler stops when there are no more new addresses to visit in its queue. The result of the web crawler's work is usually a list of links, which can be additionally filtered, depending on the needs.
Using web scraping in SEO analysis
Web scraping is of great importance when it comes to creating website audits . Specialized tools such as Screaming Frog scan and save in their databases all the relevant information regarding the technical aspects of the audited page, which an experienced technical SEO specialist can use to adjust the site to the requirements set by Google.
Web scraping techniques can also be used to create quite useful tools that streamline work. While working at KS, I managed to create several tools that use this data acquisition technique and they were used for, among other things:
checking language consistency within a given website - if a website has several language versions, the tool uses the collected data to suggest which subpage contains language inconsistencies, e.g. in HTML attributes and tags or in the content itself. The generated report may also include information on whether a given subpage contains links to other subpages in a different language version in the article content.
checking the possibilities of internal linking - data collected by the web scraper, appropriately processed using NLP algorithms or analytical methods, allows you to discover new possibilities of internal linking within the website.
creating an article brief based on the competition in the top 10 - you can easily create a tool that will automatically show the structure of the headings for the top 10 pages for a given query in Google. Such data can be used to create a brief for a copywriter. A similar tool exists, and in a much more advanced form. It is known in the SEO industry as Surfer SEO.
