A scraper site is a website that copies content from other websites using web scraping. The content is then mirrored with the goal of creating revenue, usually through advertising and sometimes by selling user data. Scraper sites come in various forms. Some provide little, if any material or information, and are intended to obtain user information such as e-mail addresses, to be targeted for spam e-mail. Price aggregation and shopping sites access multiple listings of a product and allow a user to rapidly compare the prices.
Examples of scraper websites
Search engines such as Google could be considered a type of scraper site. Search engines gather content from other websites, save it in their own databases, index it and present the scraped content to their search engine’s own users. The majority of content scraped by search engines is copyrighted.
The scraping technique has been used on various dating websites as well and they often combine it with facial recognition.
Scraping is also used on general image recognition websites, and websites specifically made to identify images of crops with pests and diseases
Made for advertising
Some scraper sites are created to make money by using advertising programs. In such case, they are called Made for AdSense sites or MFA. This derogatory term refers to websites that have no redeeming value except to lure visitors to the website for the sole purpose of clicking on advertisements.
Made for AdSense sites are considered search engine spam that dilute the search results with less-than-satisfactory search results. The scraped content is redundant to that which would be shown by the search engine under normal circumstances, had no MFA website been found in the listings.
Some scraper sites link to other sites to improve their search engine ranking through a private blog network. Prior to Google’s update to its search algorithm known as Panda, a type of scraper site known as an auto blog was quite common among black hat marketers who used a method known as spamdexing.
Scraper sites may violate copyright law. Even taking content from an open content site can be a copyright violation, if done in a way which does not respect the license. For instance, the GNU Free Documentation License (GFDL) and Creative Commons ShareAlike (CC-BY-SA) licenses used on Wikipedia require that a republisher of Wikipedia inform its readers of the conditions on these licenses, and give credit to the original author.
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Depending upon the objective of a scraper, the methods in which websites are targeted differ. For example, sites with large amounts of content such as airlines, consumer electronics, department stores, etc. might be routinely targeted by their competition just to stay abreast of pricing information. bandarq online
Another type of scraper will pull snippets and text from websites that rank high for keywords they have targeted. This way they hope to rank highly in the search engine results pages (SERPs), piggybacking on the original page’s page rank. RSS feeds are vulnerable to scrapers.
Other scraper sites consist of advertisements and paragraphs of words randomly selected from a dictionary. Often a visitor will click on a pay-per-click advertisement on such site because it is the only comprehensible text on the page. Operators of these scraper sites gain financially from these clicks. Advertising networks claim to be constantly working to remove these sites from their programs, although these networks benefit directly from the clicks generated at this kind of site. From the advertisers’ point of view, the networks don’t seem to be making enough effort to stop this problem.
Scrapers tend to be associated with link farms and are sometimes perceived as the same thing, when multiple scrapers link to the same target site. A frequent target victim site might be accused of link-farm participation, due to the artificial pattern of incoming links to a victim website, linked from multiple scraper sites.
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Some programmers who create scraper sites may purchase a recently expired domain name to reuse its SEO power in Google. Whole businesses focus on understanding all expired domains and utilising them for their historical ranking ability exist. Doing so will allow SEOs to utilize the already-established backlinks to the domain name. Some spammers may try to match the topic of the expired site or copy the existing content from the Internet Archive to maintain the authenticity of the site so that the backlinks don’t drop. For example, an expired website about a photographer may be re-registered to create a site about photography tips or use the domain name in their private blog network to power their own photography site.
Services at some expired domain name registration agents provide both the facility to find these expired domains and to gather the html that the domain name used to have on its web site.
Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program.
Normally, data transfer between programs is accomplished using data structures suited for automated processing by computers, not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed, and keep ambiguity to a minimum. Very often, these transmissions are not human-readable at all.
Thus, the key element that distinguishes data scraping from regular parsing is that the output being scraped is intended for display to an end-user, rather than as input to another program, and is therefore usually neither documented nor structured for convenient parsing. Data scraping often involves ignoring binary data (usually images or multimedia data), display formatting, redundant labels, superfluous commentary, and other information which is either irrelevant or hinders automated processing.
Data scraping is most often done either to interface to a legacy system, which has no other mechanism which is compatible with current hardware, or to interface to a third-party system which does not provide a more convenient API. In the second case, the operator of the third-party system will often see screen scraping as unwanted, due to reasons such as increased system load, the loss of advertisement revenue, or the loss of control of the information content.
Data scraping is generally considered an ad hoc, inelegant technique, often used only as a “last resort” when no other mechanism for data interchange is available. Aside from the higher programming and processing overhead, output displays intended for human consumption often change structure frequently. Humans can cope with this easily, but a computer program may report nonsense, have been told to read data in a particular format or from a particular place, and with no knowledge of how to check its results for validity.
A screen fragment and a screen-scraping interface (blue box with red arrow) to customize data capture process.
Screen scraping is normally associated with the programmatic collection of visual data from a source, instead of parsing data as in Web scraping. Originally, screen scraping referred to the practice of reading text data from a computer display terminal’s screen. This was generally done by reading the terminal’s memory through its auxiliary port, or by connecting the terminal output port of one computer system to an input port on another. The term screen scraping is also commonly used to refer to the bidirectional exchange of data. This could be the simple cases where the controlling program navigates through the user interface, or more complex scenarios where the controlling program is entering data into an interface meant to be used by a human.
As a concrete example of a classic screen scraper, consider a hypothetical legacy system dating from the 1960s—the dawn of computerized data processing. Computer to user interfaces from that era were often simply text-based dumb terminals which were not much more than virtual teleprinters (such systems are still in use today, for various reasons). The desire to interface such a system to more modern systems is common. A robust solution will often require things no longer available, such as source code, system documentation, APIs, or programmers with experience in a 50-year-old computer system. In such cases, the only feasible solution may be to write a screen scraper which “pretends” to be a user at a terminal. The screen scraper might connect to the legacy system via Telnet, emulate the keystrokes needed to navigate the old user interface, process the resulting display output, extract the desired data, and pass it on to the modern system. (A sophisticated and resilient implementation of this kind, built on a platform providing the governance and control required by a major enterprise—e.g. change control, security, user management, data protection, operational audit, load balancing and queue management, etc.—could be said to be an example of robotic process automation software.)
In the 1980s, financial data providers such as Reuters, Telerate, and Quotron displayed data in 24×80 format intended for a human reader. Users of this data, particularly investment banks, wrote applications to capture and convert this character data as numeric data for inclusion into calculations for trading decisions without re-keying the data. The common term for this practice, especially in the United Kingdom, was page shredding, since the results could be imagined to have passed through a paper shredder. Internally Reuters used the term ‘logicized’ for this conversion process, running a sophisticated computer system on VAX/VMS called the Logicizer.
More modern screen scraping techniques include capturing the bitmap data from the screen and running it through an OCR engine, or for some specialised automated testing systems, matching the screen’s bitmap data against expected results. This can be combined in the case of GUI applications, with querying the graphical controls by programmatically obtaining references to their underlying programming objects. A sequence of screens is automatically captured and converted into a database.
Another modern adaptation to these techniques is to use, instead of a sequence of screens as input, a set of images or PDF files, so there are some overlaps with generic “document scraping” and report mining techniques.
Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an API or tool to extract data from a web site. Companies like Amazon AWS and Google provide web scraping tools, services and public data available free of cost to end users. Newer forms of web scraping involve listening to data feeds from web servers. For example, JSON is commonly used as a transport storage mechanism between the client and the web server.
Recently, companies have developed web scraping systems that rely on using techniques in DOM parsing, computer vision and natural language processing to simulate the human processing that occurs when viewing a webpage to automatically extract useful information.
Large websites usually use defensive algorithms to protect their data from web scrapers and to limit the number of requests an IP or IP network may send. This has caused an ongoing battle between website developers and scraping developers.
Report mining is the extraction of data from human readable computer reports. Conventional data extraction requires a connection to a working source system, suitable connectivity standards or an API, and usually complex querying. By using the source system’s standard reporting options, and directing the output to a spool file instead of to a printer, static reports can be generated suitable for offline analysis via report mining. This approach can avoid intensive CPU usage during business hours, can minimise end-user licence costs for ERP customers, and can offer very rapid prototyping and development of custom reports. Whereas data scraping and web scraping involve interacting with dynamic output, report mining involves extracting data from files in a human readable format, such as HTML, PDF, or text. These can be easily generated from almost any system by intercepting the data feed to a printer. This approach can provide a quick and simple route to obtaining data without needing to program an API to the source system.