Search engine optimization (SEO) is the process of improving the visibility of a website or a web page in search engines' "natural," or un-paid ("organic" or "algorithmic"), search results. In general, the earlier (or higher ranked on the search results page), and more frequently a site appears in the search results list, the more visitors it will receive from the search engine's users. SEO may target different kinds of search, including image search, local search, video search, academic search,^[1] news search and industry-specific vertical search engines.

As an Internet marketing strategy, SEO considers how search engines work, what people search for, the actual search terms or keywords typed into search engines and which search engines are preferred by their targeted audience. Optimizing a website may involve editing its content and HTML and associated coding to both increase its relevance to specific keywords and to remove barriers to the indexing activities of search engines. Promoting a site to increase the number of backlinks, or inbound links, is another SEO tactic.

The acronym "SEOs" can refer to "search engine optimizers," a term adopted by an industry of consultants who carry out optimization projects on behalf of clients, and by employees who perform SEO services in-house. Search engine optimizers may offer SEO as a stand-alone service or as a part of a broader marketing campaign. Because effective SEO may require changes to the HTML source code of a site and site content, SEO tactics may be incorporated into website development and design. The term "search engine friendly" may be used to describe website designs, menus, content management systems, images, videos, shopping carts, and other elements that have been optimized for the purpose of search engine exposure.

History[link]

Webmasters and content providers began optimizing sites for search engines in the mid-1990s, as the first search engines were cataloging the early Web. Initially, all webmasters needed to do was to submit the address of a page, or URL, to the various engines which would send a "spider" to "crawl" that page, extract links to other pages from it, and return information found on the page to be indexed.^[2] The process involves a search engine spider downloading a page and storing it on the search engine's own server, where a second program, known as an indexer, extracts various information about the page, such as the words it contains and where these are located, as well as any weight for specific words, and all links the page contains, which are then placed into a scheduler for crawling at a later date.

Site owners started to recognize the value of having their sites highly ranked and visible in search engine results, creating an opportunity for both white hat and black hat SEO practitioners. According to industry analyst Danny Sullivan, the phrase "search engine optimization" probably came into use in 1997.^[3] The first documented use of the term Search Engine Optimization was John Audette and his company Multimedia Marketing Group as documented by a web page from the MMG site from August, 1997.^[4]

Early versions of search algorithms relied on webmaster-provided information such as the keyword meta tag, or index files in engines like ALIWEB. Meta tags provide a guide to each page's content. Using meta data to index pages was found to be less than reliable, however, because the webmaster's choice of keywords in the meta tag could potentially be an inaccurate representation of the site's actual content. Inaccurate, incomplete, and inconsistent data in meta tags could and did cause pages to rank for irrelevant searches.^{[5][unreliable source?]} Web content providers also manipulated a number of attributes within the HTML source of a page in an attempt to rank well in search engines.^[6]

By relying so much on factors such as keyword density which were exclusively within a webmaster's control, early search engines suffered from abuse and ranking manipulation. To provide better results to their users, search engines had to adapt to ensure their results pages showed the most relevant search results, rather than unrelated pages stuffed with numerous keywords by unscrupulous webmasters. Since the success and popularity of a search engine is determined by its ability to produce the most relevant results to any given search, allowing those results to be false would turn users to find other search sources. Search engines responded by developing more complex ranking algorithms, taking into account additional factors that were more difficult for webmasters to manipulate. Graduate students at Stanford University, Larry Page and Sergey Brin, developed "Backrub," a search engine that relied on a mathematical algorithm to rate the prominence of web pages. The number calculated by the algorithm, PageRank, is a function of the quantity and strength of inbound links.^[7] PageRank estimates the likelihood that a given page will be reached by a web user who randomly surfs the web, and follows links from one page to another. In effect, this means that some links are stronger than others, as a higher PageRank page is more likely to be reached by the random surfer.

Page and Brin founded Google in 1998. Google attracted a loyal following among the growing number of Internet users, who liked its simple design.^[8] Off-page factors (such as PageRank and hyperlink analysis) were considered as well as on-page factors (such as keyword frequency, meta tags, headings, links and site structure) to enable Google to avoid the kind of manipulation seen in search engines that only considered on-page factors for their rankings. Although PageRank was more difficult to game, webmasters had already developed link building tools and schemes to influence the Inktomi search engine, and these methods proved similarly applicable to gaming PageRank. Many sites focused on exchanging, buying, and selling links, often on a massive scale. Some of these schemes, or link farms, involved the creation of thousands of sites for the sole purpose of link spamming.^[9]

By 2004, search engines had incorporated a wide range of undisclosed factors in their ranking algorithms to reduce the impact of link manipulation. Google says it ranks sites using more than 200 different signals.^[10] The leading search engines, Google, Bing, and Yahoo, do not disclose the algorithms they use to rank pages. SEO service providers, such as Rand Fishkin, Barry Schwartz, Aaron Wall and Jill Whalen, have studied different approaches to search engine optimization, and have published their opinions in online forums and blogs.^[11][12] SEO practitioners may also study patents held by various search engines to gain insight into the algorithms.^[13]

In 2005, Google began personalizing search results for each user. Depending on their history of previous searches, Google crafted results for logged in users.^[14] In 2008, Bruce Clay said that "ranking is dead" because of personalized search. It would become meaningless to discuss how a website ranked, because its rank would potentially be different for each user and each search.^[15]

In 2007, Google announced a campaign against paid links that transfer PageRank.^[16] On June 15, 2009, Google disclosed that they had taken measures to mitigate the effects of PageRank sculpting by use of the nofollow attribute on links. Matt Cutts, a well-known software engineer at Google, announced that Google Bot would no longer treat nofollowed links in the same way, in order to prevent SEO service providers from using nofollow for PageRank sculpting.^[17] As a result of this change the usage of nofollow leads to evaporation of pagerank. In order to avoid the above, SEO engineers developed alternative techniques that replace nofollowed tags with obfuscated Javascript and thus permit PageRank sculpting. Additionally several solutions have been suggested that include the usage of iframes, Flash and Javascript. ^[18]

In December 2009, Google announced it would be using the web search history of all its users in order to populate search results.^[19]

Google Instant, real-time-search, was introduced in late 2010 in an attempt to make search results more timely and relevant. Historically site administrators have spent months or even years optimizing a website to increase search rankings. With the growth in popularity of social media sites and blogs the leading engines made changes to their algorithms to allow fresh content to rank quickly within the search results.^[20]

In February 2011, Google announced the "Panda update, which penalizes websites containing content duplicated from other websites and sources. Historically websites have copied content from one another and benefited in search engine rankings by engaging in this practice, however Google implemented a new system which punishes sites whose content is not unique. ^[21]

Relationship with search engines[link]

By 1997, search engines recognized that webmasters were making efforts to rank well in their search engines, and that some webmasters were even manipulating their rankings in search results by stuffing pages with excessive or irrelevant keywords. Early search engines, such as Altavista and Infoseek, adjusted their algorithms in an effort to prevent webmasters from manipulating rankings.^[22]

Due to the high marketing value of targeted search results, there is potential for an adversarial relationship between search engines and SEO service providers. In 2005, an annual conference, AIRWeb, Adversarial Information Retrieval on the Web,^[23] was created to discuss and minimize the damaging effects of aggressive web content providers.

Companies that employ overly aggressive techniques can get their client websites banned from the search results. In 2005, the Wall Street Journal reported on a company, Traffic Power, which allegedly used high-risk techniques and failed to disclose those risks to its clients.^[24] Wired magazine reported that the same company sued blogger and SEO Aaron Wall for writing about the ban.^[25] Google's Matt Cutts later confirmed that Google did in fact ban Traffic Power and some of its clients.^[26]

Some search engines have also reached out to the SEO industry, and are frequent sponsors and guests at SEO conferences, chats, and seminars. Major search engines provide information and guidelines to help with site optimization.^[27][28] Google has a Sitemaps program to help webmasters learn if Google is having any problems indexing their website and also provides data on Google traffic to the website.^[29] Bing Toolbox provides a way from webmasters to submit a sitemap and web feeds, allowing users to determine the crawl rate, and how many pages have been indexed by their search engine.

Methods[link]

Suppose each circle is a website, and an arrow is a link from one website to another, such that a user can click on a link within, say, website F to go to website B, but not vice versa. Search engines begin by assuming that each website has an equal chance of being chosen by a user. Next, crawlers examine which websites link to which other websites and guess that websites with more incoming links contain valuable information that users want.

Search engines uses complex mathematical algorithms to guess which websites a user seeks, based in part on examination of how websites link to each other. Since website B is the recipient of numerous inbound links, B ranks highly in a web search, and will come up early in a web search. Further, since B is popular, and has an outbound link to C, C ranks highly too.

Getting indexed[link]

The leading search engines, such as Google, Bing and Yahoo!, use crawlers to find pages for their algorithmic search results. Pages that are linked from other search engine indexed pages do not need to be submitted because they are found automatically. Some search engines, notably Yahoo!, operate a paid submission service that guarantee crawling for either a set fee or cost per click.^[30] Such programs usually guarantee inclusion in the database, but do not guarantee specific ranking within the search results.^[31] Two major directories, the Yahoo Directory and the Open Directory Project both require manual submission and human editorial review.^[32] Google offers Google Webmaster Tools, for which an XML Sitemap feed can be created and submitted for free to ensure that all pages are found, especially pages that are not discoverable by automatically following links.^[33]

Search engine crawlers may look at a number of different factors when crawling a site. Not every page is indexed by the search engines. Distance of pages from the root directory of a site may also be a factor in whether or not pages get crawled.^[34]

Preventing crawling[link]

To avoid undesirable content in the search indexes, webmasters can instruct spiders not to crawl certain files or directories through the standard robots.txt file in the root directory of the domain. Additionally, a page can be explicitly excluded from a search engine's database by using a meta tag specific to robots. When a search engine visits a site, the robots.txt located in the root directory is the first file crawled. The robots.txt file is then parsed, and will instruct the robot as to which pages are not to be crawled. As a search engine crawler may keep a cached copy of this file, it may on occasion crawl pages a webmaster does not wish crawled. Pages typically prevented from being crawled include login specific pages such as shopping carts and user-specific content such as search results from internal searches. In March 2007, Google warned webmasters that they should prevent indexing of internal search results because those pages are considered search spam.^[35]

Increasing prominence[link]

A variety of methods can increase the prominence of a webpage within the search results. Cross linking between pages of the same website to provide more links to most important pages may improve its visibility.^[36] Writing content that includes frequently searched keyword phrase, so as to be relevant to a wide variety of search queries will tend to increase traffic.^[36] Updating content so as to keep search engines crawling back frequently can give additional weight to a site. Adding relevant keywords to a web page's meta data, including the title tag and meta description, will tend to improve the relevancy of a site's search listings, thus increasing traffic. URL normalization of web pages accessible via multiple urls, using the "canonical" meta tag^[37] or via 301 redirects can help make sure links to different versions of the url all count towards the page's link popularity score.

White hat versus black hat[link]

SEO techniques can be classified into two broad categories: techniques that search engines recommend as part of good design, and those techniques of which search engines do not approve. The search engines attempt to minimize the effect of the latter, among them spamdexing. Industry commentators have classified these methods, and the practitioners who employ them, as either white hat SEO, or black hat SEO.^[38] White hats tend to produce results that last a long time, whereas black hats anticipate that their sites may eventually be banned either temporarily or permanently once the search engines discover what they are doing.^[39]

An SEO technique is considered white hat if it conforms to the search engines' guidelines and involves no deception. As the search engine guidelines^[27][28][40] are not written as a series of rules or commandments, this is an important distinction to note. White hat SEO is not just about following guidelines, but is about ensuring that the content a search engine indexes and subsequently ranks is the same content a user will see. White hat advice is generally summed up as creating content for users, not for search engines, and then making that content easily accessible to the spiders, rather than attempting to trick the algorithm from its intended purpose. White hat SEO is in many ways similar to web development that promotes accessibility,^[41] although the two are not identical.

Black hat SEO attempts to improve rankings in ways that are disapproved of by the search engines, or involve deception. One black hat technique uses text that is hidden, either as text colored similar to the background, in an invisible div, or positioned off screen. Another method gives a different page depending on whether the page is being requested by a human visitor or a search engine, a technique known as cloaking.

Search engines may penalize sites they discover using black hat methods, either by reducing their rankings or eliminating their listings from their databases altogether. Such penalties can be applied either automatically by the search engines' algorithms, or by a manual site review. One example was the February 2006 Google removal of both BMW Germany and Ricoh Germany for use of deceptive practices.^[42] Both companies, however, quickly apologized, fixed the offending pages, and were restored to Google's list.^[43]

As a marketing strategy[link]

SEO is not an appropriate strategy for every website, and other Internet marketing strategies can be more effective, depending on the site operator's goals.^[44] A successful Internet marketing campaign may also depend upon building high quality web pages to engage and persuade, setting up analytics programs to enable site owners to measure results, and improving a site's conversion rate.^[45]

SEO may generate an adequate return on investment. However, search engines are not paid for organic search traffic, their algorithms change, and there are no guarantees of continued referrals. Due to this lack of guarantees and certainty, a business that relies heavily on search engine traffic can suffer major losses if the search engines stop sending visitors.^[46] Search engines can change their algorithms, impacting a website's placement, possibly resulting in a serious loss of traffic. According to Google's CEO, Eric Schmidt, in 2010, Google made over 500 algorithm changes - almost 1.5 per day.^[47] It is considered wise business practice for website operators to liberate themselves from dependence on search engine traffic.^[48] Seomoz.org has suggested that "search marketers, in a twist of irony, receive a very small share of their traffic from search engines." Instead, their main sources of traffic are links from other websites.^[49]

International markets[link]

Optimization techniques are highly tuned to the dominant search engines in the target market. The search engines' market shares vary from market to market, as does competition. In 2003, Danny Sullivan stated that Google represented about 75% of all searches.^[50] In markets outside the United States, Google's share is often larger, and Google remains the dominant search engine worldwide as of 2007.^[51] As of 2006, Google had an 85-90% market share in Germany.^[52] While there were hundreds of SEO firms in the US at that time, there were only about five in Germany.^[52] As of June 2008, the marketshare of Google in the UK was close to 90% according to Hitwise.^[53] That market share is achieved in a number of countries.

As of 2009, there are only a few large markets where Google is not the leading search engine. In most cases, when Google is not leading in a given market, it is lagging behind a local player. The most notable markets where this is the case are China, Japan, South Korea, Russia and the Czech Republic where respectively Baidu, Yahoo! Japan, Naver, Yandex and Seznam are market leaders.

Successful search optimization for international markets may require professional translation of web pages, registration of a domain name with a top level domain in the target market, and web hosting that provides a local IP address. Otherwise, the fundamental elements of search optimization are essentially the same, regardless of language.^[52]

Legal precedents[link]

On October 17, 2002, SearchKing filed suit in the United States District Court, Western District of Oklahoma, against the search engine Google. SearchKing's claim was that Google's tactics to prevent spamdexing constituted a tortious interference with contractual relations. On May 27, 2003, the court granted Google's motion to dismiss the complaint because SearchKing "failed to state a claim upon which relief may be granted."^[54][55]

In March 2006, KinderStart filed a lawsuit against Google over search engine rankings. Kinderstart's website was removed from Google's index prior to the lawsuit and the amount of traffic to the site dropped by 70%. On March 16, 2007 the United States District Court for the Northern District of California (San Jose Division) dismissed KinderStart's complaint without leave to amend, and partially granted Google's motion for Rule 11 sanctions against KinderStart's attorney, requiring him to pay part of Google's legal expenses.^[56][57]

See also[link]

• List of search engines
• Search engine marketing
• Search engine optimization copywriting
• Trackback submitter
• Website promotion

Notes[link]

External links[link]

Listen to this article (info/dl)

This audio file was created from a revision of the "Search engine optimization" article dated 2008-05-20, and does not reflect subsequent edits to the article. (Audio help)

More spoken articles

• Google Webmaster Guidelines
• Yahoo! Webmaster Guidelines
• "The Dirty Little Secrets of Search," article in The New York Times (February 12, 2011)
• Google I/O 2010 - SEO site advice from the experts on YouTube - Technical tutorial on search engine optimization, given at Google I/O 2010.

A web search engine is designed to search for information on the World Wide Web. The search results are generally presented in a list of results often referred to as search engine results pages (SERPs). The information may consist of web pages, images, information and other types of files. Some search engines also mine data available in databases or open directories. Unlike web directories, which are maintained only by human editors, search engines also maintain real-time information by running an algorithm on a web crawler.

History[link]

During the early development of the web, there was a list of webservers edited by Tim Berners-Lee and hosted on the CERN webserver. One historical snapshot from 1992 remains.^[1] As more webservers went online the central list could not keep up. On the NCSA site new servers were announced under the title "What's New!"^[2]

The very first tool used for searching on the Internet was Archie.^[3] The name stands for "archive" without the "v". It was created in 1990 by Alan Emtage, Bill Heelan and J. Peter Deutsch, computer science students at McGill University in Montreal. The program downloaded the directory listings of all the files located on public anonymous FTP (File Transfer Protocol) sites, creating a searchable database of file names; however, Archie did not index the contents of these sites since the amount of data was so limited it could be readily searched manually.

The rise of Gopher (created in 1991 by Mark McCahill at the University of Minnesota) led to two new search programs, Veronica and Jughead. Like Archie, they searched the file names and titles stored in Gopher index systems. Veronica (Very Easy Rodent-Oriented Net-wide Index to Computerized Archives) provided a keyword search of most Gopher menu titles in the entire Gopher listings. Jughead (Jonzy's Universal Gopher Hierarchy Excavation And Display) was a tool for obtaining menu information from specific Gopher servers. While the name of the search engine "Archie" was not a reference to the Archie comic book series, "Veronica" and "Jughead" are characters in the series, thus referencing their predecessor.

In the summer of 1993, no search engine existed yet for the web, though numerous specialized catalogues were maintained by hand. Oscar Nierstrasz at the University of Geneva wrote a series of Perl scripts that would periodically mirror these pages and rewrite them into a standard format which formed the basis for W3Catalog, the web's first primitive search engine, released on September 2, 1993.^[4]

In June 1993, Matthew Gray, then at MIT, produced what was probably the first web robot, the Perl-based World Wide Web Wanderer, and used it to generate an index called 'Wandex'. The purpose of the Wanderer was to measure the size of the World Wide Web, which it did until late 1995. The web's second search engine Aliweb appeared in November 1993. Aliweb did not use a web robot, but instead depended on being notified by website administrators of the existence at each site of an index file in a particular format.

JumpStation (released in December 1993^[5]) used a web robot to find web pages and to build its index, and used a web form as the interface to its query program. It was thus the first WWW resource-discovery tool to combine the three essential features of a web search engine (crawling, indexing, and searching) as described below. Because of the limited resources available on the platform on which it ran, its indexing and hence searching were limited to the titles and headings found in the web pages the crawler encountered.

One of the first "full text" crawler-based search engines was WebCrawler, which came out in 1994. Unlike its predecessors, it let users search for any word in any webpage, which has become the standard for all major search engines since. It was also the first one to be widely known by the public. Also in 1994, Lycos (which started at Carnegie Mellon University) was launched and became a major commercial endeavor.

Soon after, many search engines appeared and vied for popularity. These included Magellan, Excite, Infoseek, Inktomi, Northern Light, and AltaVista. Yahoo! was among the most popular ways for people to find web pages of interest, but its search function operated on its web directory, rather than full-text copies of web pages. Information seekers could also browse the directory instead of doing a keyword-based search.

In 1996, Netscape was looking to give a single search engine an exclusive deal to be the featured search engine on Netscape's web browser. There was so much interest that instead a deal was struck with Netscape by five of the major search engines, where for $5 million per year each search engine would be in rotation on the Netscape search engine page. The five engines were Yahoo!, Magellan, Lycos, Infoseek, and Excite.^[6][7]

Search engines were also known as some of the brightest stars in the Internet investing frenzy that occurred in the late 1990s.^[8] Several companies entered the market spectacularly, receiving record gains during their initial public offerings. Some have taken down their public search engine, and are marketing enterprise-only editions, such as Northern Light. Many search engine companies were caught up in the dot-com bubble, a speculation-driven market boom that peaked in 1999 and ended in 2001.

Around 2000, Google's search engine rose to prominence.^{[citation needed]} The company achieved better results for many searches with an innovation called PageRank. This iterative algorithm ranks web pages based on the number and PageRank of other web sites and pages that link there, on the premise that good or desirable pages are linked to more than others. Google also maintained a minimalist interface to its search engine. In contrast, many of its competitors embedded a search engine in a web portal.

By 2000, Yahoo! was providing search services based on Inktomi's search engine. Yahoo! acquired Inktomi in 2002, and Overture (which owned AlltheWeb and AltaVista) in 2003. Yahoo! switched to Google's search engine until 2004, when it launched its own search engine based on the combined technologies of its acquisitions.

Microsoft first launched MSN Search in the fall of 1998 using search results from Inktomi. In early 1999 the site began to display listings from Looksmart blended with results from Inktomi except for a short time in 1999 when results from AltaVista were used instead. In 2004, Microsoft began a transition to its own search technology, powered by its own web crawler (called msnbot).

Microsoft's rebranded search engine, Bing, was launched on June 1, 2009. On July 29, 2009, Yahoo! and Microsoft finalized a deal in which Yahoo! Search would be powered by Microsoft Bing technology.

How web search engines work[link]

A search engine operates in the following order:

1. Web crawling
2. Indexing
3. Searching

Web search engines work by storing information about many web pages, which they retrieve from the HTML itself. These pages are retrieved by a Web crawler (sometimes also known as a spider) — an automated Web browser which follows every link on the site. Exclusions can be made by the use of robots.txt. The contents of each page are then analyzed to determine how it should be indexed (for example, words can be extracted from the titles,page content, headings, or special fields called meta tags). Data about web pages are stored in an index database for use in later queries. A query can be a single word. The purpose of an index is to allow information to be found as quickly as possible. Some search engines, such as Google, store all or part of the source page (referred to as a cache) as well as information about the web pages, whereas others, such as AltaVista, store every word of every page they find. This cached page always holds the actual search text since it is the one that was actually indexed, so it can be very useful when the content of the current page has been updated and the search terms are no longer in it. This problem might be considered to be a mild form of linkrot, and Google's handling of it increases usability by satisfying user expectations that the search terms will be on the returned webpage. This satisfies the principle of least astonishment since the user normally expects the search terms to be on the returned pages. Increased search relevance makes these cached pages very useful, even beyond the fact that they may contain data that may no longer be available elsewhere.

High-level architecture of a standard Web crawler

When a user enters a query into a search engine (typically by using keywords), the engine examines its index and provides a listing of best-matching web pages according to its criteria, usually with a short summary containing the document's title and sometimes parts of the text. The index is built from the information stored with the data and the method by which the information is indexed. Unfortunately, there are currently no known public search engines that allow documents to be searched by date. Most search engines support the use of the boolean operators AND, OR and NOT to further specify the search query. Boolean operators are for literal searches that allow the user to refine and extend the terms of the search. The engine looks for the words or phrases exactly as entered. Some search engines provide an advanced feature called proximity search which allows users to define the distance between keywords. There is also concept-based searching where the research involves using statistical analysis on pages containing the words or phrases you search for. As well, natural language queries allow the user to type a question in the same form one would ask it to a human. A site like this would be ask.com.

The usefulness of a search engine depends on the relevance of the result set it gives back. While there may be millions of web pages that include a particular word or phrase, some pages may be more relevant, popular, or authoritative than others. Most search engines employ methods to rank the results to provide the "best" results first. How a search engine decides which pages are the best matches, and what order the results should be shown in, varies widely from one engine to another. The methods also change over time as Internet usage changes and new techniques evolve. There are two main types of search engine that have evolved: one is a system of predefined and hierarchically ordered keywords that humans have programmed extensively. The other is a system that generates an "inverted index" by analyzing texts it locates. This second form relies much more heavily on the computer itself to do the bulk of the work.

Most Web search engines are commercial ventures supported by advertising revenue and, as a result, some employ the practice of allowing advertisers to pay money to have their listings ranked higher in search results. Those search engines which do not accept money for their search engine results make money by running search related ads alongside the regular search engine results. The search engines make money every time someone clicks on one of these ads.

Market share[link]

This section requires expansion with: Information about national search engines like StatCounter, Yandex, Naver and their market share in respective countries..

Google's worldwide market share peaked at 86.3% in April 2010.^[10] Yahoo!, Bing and other search engines are more popular in the US than in Europe.

According to Hitwise, market share in the U.S. for October 2011 was Google 65.38%, Bing-powered (Bing and Yahoo!) 28.62%, and the remaining 66 search engines 6%. However, an Experian Hit wise report released in August 2011 gave the "success rate" of searches sampled in July. Over 80 percent of Yahoo! and Bing searches resulted in the users visiting a web site, while Google's rate was just under 68 percent.^[11][12]

In the People's Republic of China, Baidu held a 61.6% market share for web search in July 2009.^[13] In Russian Federation, Yandex holds around 60% of the market share as of April 2012.^[14]

Search engine bias[link]

Although search engines are programmed to rank websites based on their popularity and relevancy, empirical studies indicate various political, economic, and social biases in the information they provide.^[15][16] These biases could be a direct result of economic and commercial processes (e.g., companies that advertise with a search engine can become also more popular in its organic search results), and political processes (e.g., the removal of search results in order to comply with local laws).^[17] Google Bombing is one example of an attempt to manipulate search results for political, social or commercial reasons.

Customized results and filter bubbles[link]

Many search engines such as Google and Bing provide customized results based on the user's activity history. This leads to an effect that has been called a filter bubble. The term describes a phenomenon in which websites use algorithms to selectively guess what information a user would like to see, based on information about the user (such as location, past click behaviour and search history). As a result, websites tend to show only information which agrees with the user's past viewpoint, effectively isolating the user in a bubble that tends to exclude contrary information. Prime examples are Google's personalized search results and Facebook's personalized news stream. According to Eli Pariser, who coined the term, users get less exposure to conflicting viewpoints and are isolated intellectually in their own informational bubble. Pariser related an example in which one user searched Google for "BP" and got investment news about British Petroleum while another searcher got information about the Deepwater Horizon oil spill and that the two search results pages were "strikingly different."^[18][19][20] The bubble effect may have negative implications for civic discourse, according to Pariser, but there are contrasting views suggesting the effect is minimal and addressable.^[21]

Since this problem has been identified, competing search engines have emerged that seek to avoid this problem by not tracking^[22] or "bubbling"^[23] users. One example is DuckDuckGo.

See also[link]

References[link]

This article's citation style may be unclear. The references used may be made clearer with a different or consistent style of citation, footnoting, or external linking. (September 2009)

• GBMW: Reports of 30-day punishment, re: Car maker BMW had its German website bmw.de delisted from Google, such as: Slashdot-BMW (05-Feb-2006).
• INSIZ: Maximum size of webpages indexed by MSN/Google/Yahoo! ("100-kb limit"): Max Page-size (28-Apr-2006).

Further reading[link]

• For a more detailed history of early search engines, see Search Engine Birthdays (from Search Engine Watch), Chris Sherman, September 2003.
• Steve Lawrence; C. Lee Giles (1999). "Accessibility of information on the web". Nature 400 (6740): 107–9. DOI:10.1038/21987. PMID 10428673. 
• Bing Liu (2007), Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, ISBN 3-540-37881-2
• Bar-Ilan, J. (2004). The use of Web search engines in information science research. ARIST, 38, 231-288.
• Levene, Mark (2005). An Introduction to Search Engines and Web Navigation. Pearson. 
• Hock, Randolph (2007). The Extreme Searcher's Handbook.  ISBN 978-0-910965-76-7
• Javed Mostafa (February 2005). "Seeking Better Web Searches". Scientific American Magazine. http://www.sciam.com/article.cfm?articleID=0006304A-37F4-11E8-B7F483414B7F0000. ^{[dead link]}
• Ross, Nancy; Wolfram, Dietmar (2000). "End user searching on the Internet: An analysis of term pair topics submitted to the Excite search engine". Journal of the American Society for Information Science 51 (10): 949–958. DOI:10.1002/1097-4571(2000)51:10<949::AID-ASI70>3.0.CO;2-5. 
• Xie, M. et al. (1998). "Quality dimensions of Internet search engines". Journal of Information Science 24 (5): 365–372. DOI:10.1177/016555159802400509. 
• Information Retrieval: Implementing and Evaluating Search Engines. MIT Press. 2010. http://www.ir.uwaterloo.ca/book/. 

External links[link]

Wikimedia Commons has media related to: Internet search engines

• Search Engines at the Open Directory Project