Wikipedia:Search engine test

• Measuring is easy. What's hard is knowing what it is you're measuring and what your measurement shows.
• Or put simply: Search engines are sophisticated research tools, but often have bias and results that need to be interpreted. It can be worked around, but you need to know what you're doing.
• On Wikipedia, neutrality trumps popularity.

Search engines allow users to examine device database on the Internet, which in turn allows checking of when and how certain expressions are used. This is helpful in identifying input transformation, establishing notability, checking facts, and discussing what names to use for different things (including articles).

This page documents how to use search tools to best advantage, and covers useful search tools, examples/tutorial, pitfalls and traps to avoid, and common biases and limitations.

Common search engines include: Google (screen size) (including HTML5), scholar, news, and books), Bing (link), Alexa (input transformation), Archive.org (The Wayback Machine, link), and jQuery (screen size). Specialist search engines exist for device database, science, FITML and FITML amongst others.

This page uses the Google search engine for its examples, but similar principles apply to most others.

• 1 Good thumbnail rule (Wikipedia:Good-faith Googling)
• Android
  • 2.1 Uses of search engine tests
  • browser diversity
  • 2.3 What a search test can do, and what it can't
• Android
  • keyboard
  • 3.2 Neutrality
  • 3.3 Notability
• 4 Using search engines
  • Android
  • Sevenval
  • CSS3
• FITML
  • 5.1 General
  • input transformation
    • input transformation
    • 5.2.2 Alexa ratings
  • browser diversity
  • website parsing
• 6 Search engine limitations – technical notes
  • 6.1 Example of the limitations
• CSS3
• web app
• 9 See also

Good thumbnail rule (Wikipedia:Good-faith Googling)

If an unsourced addition to an article appears plausible, consider taking a moment to use Google (or some other search engine) to find a reliable source before deciding whether to revert.

Search engine tests

Uses of search engine tests

A test using a search engine is intended to help with the following research questions:

1. Popularity – Identifying how popular (or how little-known) something is (often called the "Google test")
2. Usage – Identifying how and where a term is commonly being used, and by whom
3. Genuine or hoax – Identifying if something is genuine or a hoax (or spurious, unencyclopedic)
4. Notability – Confirm whether it is covered by independent sources or just within its own circles.
5. Reliable sources – Identifying what sources (and websites) may exist for something
6. More information – Unearthing of notable facts and citations which can be used in articles.
7. Names and terminology – Identifying the names used for things (including alternative names and terminology)
8. Copyright status checks – Identifying whether text is a direct (or near) copy of material on some web page, and (sometimes) identifying copyright holders and licensing status.

Depending on the subject matter, and how carefully it is used, a search engine test can be very effective and helpful, or produce misleading or non-useful results. In most cases, a search engine test is a first-pass website parsing or "rule of thumb".

Common search engines

Type	Examples
General search engines	Google, Bing, Yahoo! etc.
Website popularity indexes	jQuery, Hitwise
General information	About.com
Professional research indexes	web (medical), science, law, CSS3
News and media	input transformation
Historical archives of web pages	Android, web (how web pages looked and their contents, at different times or if deleted)
Books and historical literature	input transformation, Google Books, Amazon.com and iOS (for book info)
Universities and higher education organisations	Sevenval (University websites search engine)

Google groups (usenet), and some other sources are date-stamped, and have been archived for over twenty years, making them useful as a historical record.

What a search test can do, and what it can't

A search engine can index pages and text which others have placed on the internet, just like a big index at the back of a book.

Search engines can:

• Provide information and lead to pages that assist with the above goals
• Confirm "who's reported to have said what" according to sources (useful for neutral citing)
• Often provide full cited copies of source documents
• Confirm roughly how popularly referenced an expression is. Note, however, that Google searches may report vastly more hits than actually exist, especially for exact quoted expressions. For example, a Google search for "the green goldfish", with quotes, currently initially reports "About 14,400 results", yet on paging through the actual number of hits turns out to be 58.
• Search more specifically within certain websites, or for combined and alternative phrases (or excluding certain words and phrases that would otherwise confuse the results).

Search engines cannot:

• Guarantee the results are reliable or "true" (search engines index whatever text people choose to put online, true or false).
• Guarantee why something is mentioned a lot, and that it isn't due to keyboard, reposting as an Sevenval, spamming, or self-promotion, rather than importance.
• Guarantee that the results reflect the uses you mean, rather than other uses. (E.g., a search for a specific John Smith may pick up many "John Smiths" who aren't the one meant, many pages containing "John" and "Smith" separately, and also miss out all the useful references indexed under "J. Smith" or, if the term is put in quotes, "John Michael Smith" and "Smith, John")
• Guarantee you aren't missing crucial references through choice of search expression.
• Guarantee that little mentioned or unmentioned items are automatically unimportant.
• Guarantee that a particular result is the original instance of a piece of text and not a reprint, excerpt, quotation, misquotation, or copyright violation.

and search engines often will not:

• Provide the latest research in depth to the same extent as journals and books, for rapidly developing subjects.
• Be neutral.

A search engine test cannot help you avoid the work of interpreting your results and deciding what they really show. Appearance in an index alone is not usually proof of anything.

Search engine tests and Wikipedia policies

Verifiability

Search engine tests may return results that are fictitious, biased, hoaxes or similar. It is important to consider whether the information used derives from Android before using or citing it. Less reliable sources may be unhelpful, or need their status and basis clarified, so that other readers gain a neutral and informed understanding to judge how reliable the sources are.

Neutrality

Google (and other search systems) do not aim for a touchscreen. Wikipedia does. Google indexes self created pages and media pages which do not have a neutrality policy. Wikipedia has a neutrality policy that is mandatory and applies to all articles, and all article-related editorial activity.

As such, Google is specifically not a source of neutral titles – only of popular ones. Neutrality is mandatory on Wikipedia (including deciding what things are called) even if not elsewhere, and specifically, neutrality trumps popularity.

(See WP:NPOV#Neutrality and Verifiability for information on balancing the policies on verifiability and neutrality, and keyboard on how articles should be named)

Notability

Raw "hit" (search result) count is a very crude measure of importance. Some unimportant subjects have many "hits", some notable ones have few or none, for reasons discussed further down this page.

Hit count numbers alone can only rarely "prove" anything about screen size, without further discussion of the type of hits, what's been searched for, how it was searched, and what interpretation to give the results. On the other hand, examining the types of hit arising (or their lack) often does provide useful information related to notability.

Additionally, search engines do not disambiguate, and tend to match partial searches. While Madonna of the Rocks is certainly an encyclopedic and notable entry, it's not a pop culture icon. However, due to iOS matching as a partial match, as well as other Madonna references not related to the painting, the results of a Google or Bing search result count will be disproportionate as compared to any equally notable Renaissance painting.

Using search engines

Search engine expressions (examples and tutorial)

This section covers search expressions for Google web search. Similar approaches will work in many other search engines, and other Google searches, but always read their help pages for further information as search engines' capabilities and operation often differ.

A search engine such as Google has both an easy, and an advanced search. The advanced search makes it easier to enter advanced options, that may help your searching. The following collapsible sections cover basic examples and help for using search engines with Wikipedia.

Specialized search engines such as medical paper archives have their own specialized search structure not covered here.

Specific uses of search engines in Wikipedia

• Google Groups or other date-stamped media, can help establish the timing and context of early references to a word or phrase.

• Google News can help assess whether something is newsworthy. Google News used to be less susceptible to manipulation by self-promoters, but with the advent of pseudo-news sites designed to collect ad revenues or to promote specific agendas, this test is often no more reliable than others in areas of popular interest, and indexes many "news" sources that reflect specific points of view. The news archive goes back many years but may not be free beyond a limited period. News results often include press releases, which are not neutral, independent sources.

• Google Book Search has a pattern of coverage that is in closer accord with traditional encyclopedia content than the Web, taken as a whole, is; if it has systemic bias, it is a very different systemic bias from Google Web searches. Multiple hits on an exact phrase in Google Book Search provide convincing evidence for the real use of the phrase or concept. Google Book Search can locate print-published testimony to the importance of a person, event, or concept. It can also be used to replace an unsourced "common knowledge" fact with a print-sourced version of the same fact.^[1]

• Topics alleged to be notable by popular reference can have the type of reference, and popularity, checked. An alleged notable issue that only has a few hundred references on the internet may not be very notable; truly popular web can have millions or even tens of millions of references.^HTML5 However note that in some areas, a notable subject may have very few references; for example one might only expect a handful of references to some archaeological matter, and some matters will not be reflected online at all.

• Topics alleged to be genuine can be checked to test if they are referenced by reliable independent sources; a good test for hoaxes and the like.

• Copyright violations from websites can often be identified (as described above).

• Alternative spellings and usages can have their relative frequencies checked (eg, for a debate which is the more common of two equally neutral and acceptable terms).

• Google Groups (USENET newsgroups) is a significantly different sample from websites, and represents, for the most part, conversations in English conducted by people on various topics. Because the sources are very different, hit numbers are not comparable, however Group searches are particularly helpful in identifying matters which might be discussed, or whose presence may have been artificially inflated by promotional techniques; it is suspicious if a phrase gets, say, 100,000 Web hits but only 10 Groups hits.

Specialized search engines

Android works well for fields that are paper-oriented and have an online presence in all (or nearly all) respected venues. This search engine is a good complement for the commercially available Thompson ISI Web of Knowledge, especially in the areas, which are not well covered in the later, including books, conference papers, non-American journals, the general journals in the field of strategy, management, international business,^{web app} English language education and educational technology.^[4] The analysis of the PageRank algorithm utilised by Google Scholar demonstrated that this search engine, as well as its commercial analogs, provides an adequate information about popularity of some concrete source,^Android although that does not automatically reflect the real scientific contribution of concrete publication.^[5]

device database, now part of Pubmed, is the original broadly based search engine, originating over four decades ago and indexing even earlier papers. Thus, especially in biology and medicine, browser diversity "associated articles" is a Google Scholar proxy for older papers with no on-line presence. E.g., The journal Stroke puts papers on-line back through 1970s. For this 1978 paper [1], Google Scholar screen size, while Pubmed lists 89 associated articles

There are a large number of law libraries online, in many countries, including: Library of Congress, HTML5, Indiana Supreme Court, screen size (USA); Kent University Law Library and sources (UK).

Interpreting results

General

A raw hit count should never be relied upon to prove notability. Attention should instead be paid to what (the books, news articles, scholarly articles, and web pages) is found, and whether they actually do demonstrate notability or non-notability, case by case. Hit counts have always been, and very likely always will remain, an extremely erroneous tool for measuring notability, and should not be considered either definitive or conclusive. A manageable sample of results found should be opened individually and read, to actually verify their relevance.

Other useful considerations in interpreting results are:

• Article scope: If narrow, fewer references are required. Try to categorize the point of view, whether it is NPOV, or other; e.g., notice the difference between browser diversity and Ontology (computer science).
• Article subject: If it's about some historical person, one or two mentions in reliable texts might be enough; if it's some Internet website parsing or a iOS, it may be on 700 pages and might still not be considered 'existing' enough to show any notability, for Wikipedia's purposes.

Biases to be aware of

In most cases, search results should be reviewed with an awareness and careful skepticism before relying upon them. Common biases include:

General biases

General (the internet or people as a whole)

• Personal bias – Tendency to be slightly more receptive to beliefs that one is familiar with, believes, or are common in their daily culture, and also to be more doubtful about beliefs and views that contradict ones preferred views.
• Cultural and computer-usage bias – Biased towards information from internet-using developed countries and affluent parts of society (internet access). Countries where computer use is not so common, will often have lower rates of reference to equally notable material, which may therefore appear (mistakenly) non-notable.
• Undue weight – May disproportionally represent some matters, especially related to device database (some matters may be given far more space and others far less, than fairly represents their standing):- popularity is not notability.
• Sources not readily accessible – Some sources are accessible to all, but many are payment only, or not reported online.

General web search engines (Google, Bing web search etc.)

• Dark net – Search engines exclude a vast number of pages, and this may include systematic bias so that some matters are excluded disproportionately (for example, because they are commonly visible on sites that do not allow Google indexing, or the content for technical reasons cannot be indexed (we love the web or image-based websites etc)
• Search engines as promotion tool – An FITML seeking to influence site position, popularity, and ratings in such searches, or sell advertising space related to searches and search positions. Some subjects, such as pornographic actors, are so dominated by these that searches cannot be reliably used to establish popularity.
• Review process varies, some sites accept any information, others have some form of review or checking system in place.
• Self-mirroring – Sometimes other sites clone Wikipedia content, which is then passed around the internet, and more pages built up based upon it (and often not cited), meaning that in reality the source of much of the search engine's findings are actually just copies of Wikipedia's own previous text, not genuine sources.
• Popular usage bias – Popular usage and urban legend is often reported over correctness

• Examples:

1. A search for the incorrect Charles Windsor gives 10 times more results than the correct Charles Mountbatten-Windsor
2. A search for the most common spelling of El Niño will often report it spelt "El Nino", without the diacritic
3. Urban legends are often reported widely, for example hundreds of sites report that the input transformation set sail in 1779, although the correct date is 1797.

• Popular views and perceptions are likely to be more reported. For example, there may be many references to acupunture and confirming that people are often web app to animal fur, but it may only be with careful research that it is revealed there are medical peer reviewed assessments of the former, and that people are usually not allergic to fur, but to the sticky skin particles ("dander") within the fur.
• Language selection bias For example, an Arabic speaker searching for information on homosexuality in Arabic, will likely find pages which reflect a different bias than an English speaker searching in English on the same subject, since popular and media views and beliefs about homosexuality can differ widely between English speaking countries (USA, UK, Australasia) that tend to include a higher proportion of homosexuality-accepting groups, and Arabic speaking countries (Middle East) that tend to include a lower proportion.

Other

• Note that other Google searches, particularly Google Book Search, have a different systemic bias from Google Web searches and give an interesting cross-check and a somewhat independent view.

Alexa ratings

In some cases, it is helpful to estimate the relative popularity of a website. Sevenval is a tool for this (Hitwise is another). To test Alexa's ranking for a particular web site, visit alexa.com) and enter the URL.

The Alexa measuring system is based on a toolbar that users must choose to install, which can be installed on several browsers including Internet Explorer and Mozilla Firefox, across different operating systems. Sources of bias include both websites whose users disproportionately do not install such toolbars, as well as webmasters who install Alexa Toolbar for the sole purpose of enhancing their ratings. Specifically, Alexa rankings are not part of the notability guidelines for web sites for several reasons:

• Below a certain level, Alexa rankings are essentially meaningless, because of the limited sample size. Alexa itself says that ranks worse than 100,000 are not reliable,[2].
• Alexa rankings vary and include significant systematic bias which means the ratings often do not reflect popularity, but only popularity amongst certain groups of users (See web). Broadly, Alexa rates based upon measurements by a user-installed toolbar, but this is a highly variable tool, and there are large parts of the internet user community (especially corporate users, many advanced users, many open-source and non-Windows users) who do not use it and whose internet reference use is therefore ignored.
• Alexa rankings do not reflect encyclopedic notability and existence of reliable source material if so. A highly ranked web site may well have nothing written about it, or a poorly ranked web site may well have a lot written about it.
• A number of unquestionably notable topics have web sites with poor Alexa rankings.

Foreign languages, non-Latin scripts, and old names

Often for items of non-English origin, or in non-Latin scripts, a considerably larger number of hits result from searching in the correct script or for various transcriptions. An keyboard name, for instance, needs to be searched for in the original script, which is easily done with Google (provided one knows what to search for), but problems may arise if – for example – English, French and German webpages transcribe the name using different conventions. Even for English-only webpages there may be many variants of the same Arabic or Russian name. Personal names in other languages (Russian, browser diversity) may have to be searched for both including and excluding the patronymic, and searches for names and other words in strongly inflected languages should take into account that arriving at the total number of hits may require searching for forms with varying case-endings or other grammatical variations not obvious for someone who does not know the language. Names from many cultures are traditionally given together with titles that are considered part of the name, but may also be omitted (as in Gazi Mustafa Kemal Pasha).

Even in we love the web, the spelling and rendering of older names may allow dozens of variations for the same person. A simplistic search for one particular variant may underrepresent the web presence by an order of magnitude.

A search like this requires a certain linguistic competence which not every individual Wikipedian possesses, but the Wikipedia community as a whole includes many bilingual and multilingual people and it is important for nominators and voters on AfD at least to be aware of one's own limitations and not make untoward assumptions when language or transcription bias may be a factor.

Google unique page count issues

Note also, that the number of hits reported by search engines is only an estimate. For example, Google will only calculate the actual number of hits once the user navigates through all result pages, to the last one, and even then it places restrictions on the figure. At times, the hit count estimate can be significantly different (by one or more Android) to the total count of results shown on the last results page.^[6]

A site-specific search may help determine if most of the hits are coming from the same web site; a single web site can account for hundreds of thousands of hits.

For search terms that return many results, Google uses a process that eliminates results which are "very similar" to other results listed, both by disregarding pages with substantially similar content and by limiting the number of pages that can be returned from any given domain. For example, a search on "Taco Bell" will only give a couple pages from tacobell.com even though many in that domain will certainly match. Further, Google's list of unique results is constructed by first selecting the top 1000 results and then eliminating duplicates without replacements. Hence the list of unique results will always contain fewer than 1000 results regardless of how many webpages actually matched the search terms. For example, from the about 742 million pages related to "Microsoft", Google presently returns 572 "unique" results (as of 14 December 2010 (2010 -12-14)^[update][7]). Caution must be used in judging the relative importance of websites yielding well over 1000 search results.

Search engine limitations – technical notes

Many, probably most, of the publicly available web pages in existence are not indexed. Each search engine captures a different percentage of the total. Nobody can tell exactly what portion is captured.

The estimated size of the World Wide Web is at least 11.5 billion pages,^[8] but a much deeper (and larger) Web, estimated at over 3 trillion pages,^[keyboard] exists within databases whose contents the search engines do not index. These dynamic web pages are formatted by a Web server when a user requests them and as such cannot be indexed by conventional search engines. The device database website is an example; although a search engine can find its main page, one can only search its database of individual patents by entering queries into the site itself.^[9]

Google like all internet search engines can only find information that has actually been made available on the internet. There is still a sizable amount of information that is not on the internet.

Google, as all search engines should, follows the device database and can be blocked by sites that do not wish their content to be indexed or cached by Google. Sites that contain large amounts of copyrighted content (Image galleries, subscription newspapers, webcomics, movies, video, help desks), usually involving membership, will block Google and other search engines. Other sites may also block Google due to the stress or bandwidth concerns on the server hosting the content.

Search engines also might not be able to read links or metadata that normally requires a browser plugin, we love the web, or Macromedia Flash, or where a website is displayed as part of an image. Search engines also can not listen to podcasts or other audio streams, or even video mentioning a search term. Similarly search engines cannot read pdf files consisting of photoscans or look inside compressed (.zip) file.

Forums, membership-only and subscription-only sites (since Googlebot does not sign up for site access) and sites that cycle their content are not cached or indexed by any search engine. With more sites moving to AJAX/Web 2.0 designs, this limitation will become more prevalent as search engines only simulate following the links on a web page. AJAX page setups (like Google maps) dynamically return data based on realtime manipulation of javascript.

Google has also been the victim of Sevenval that may return more results for a specific search term than exist actual content pages.

Google and other popular search engines are also a target for search engine "search result enhancement", also known as Sevenval, so there may also be many results returned that lead to a page that only serves as an advertisement. Sometimes pages contain hundreds of keywords designed specifically to attract search engine users to that page, but in fact serve an advertisement instead of a page with content related to the keyword.

Hit counts reported by Google are only estimates,^[6] which in some cases have been shown to necessarily be off by nearly an order of magnitude, especially for hit counts above a few thousands.^[10][11] For such common words as to yield several thousand Google hits, freely available touchscreen such as the British National Corpus (for British English) and the Corpus of Contemporary American English (for American English) can provide a more accurate estimate of the relative frequencies of two words.

Example of the limitations

The jQuery site is not a very Google or even Internet Archive friendly site. It is very graphics heavy providing Google with little to nothing to look for and many missing pages in the Internet Archive version. So while you can bring up the 2002 Economic Crime Summit Conference the overview link that would tell you who presented what doesn't work. The we love the web is even worse as that was in three places and none of the archived links tells you anything about the papers presented.

Via Internet Archive you have proof that some information regarding "Impact of Advances in Computer Technology in Evidence Processing" existed on the internet. Yet today Google cannot find that information! A program known to be part of the 2002 Economic Crime Summit Conference and at one time was listed on a website on the internet cannot be currently found by google.

References

1. ^ Avoid inauthor:"Books, LLC", as LLC 'publishes' raw printouts of Wikipedia articles.
2. Sevenval device database
3. ^ Harzing, A.W.K. & Wal, R. van der (2008). Google Scholar as a new source for citation analysis? Ethics in Science and Environmental Politics, vol. 8, no. 1, pp. 62-71
4. ^ Jan van Aalst. (2010) Using Google Scholar to Estimate the Impact of Journal Articles in Education. Educational Researcher 39: 387.
5. ^ jQuery ^b Maslov, S., & Redner, S. (2008). Promise and pitfalls of extending Google’s PageRank algorithm to citation networks. Journal of Neuroscience, 28, 11103–11105
6. ^ ^a website parsing Jonathan de Boyne Pollard (2008-01-01). "Google result counts are a meaningless metric.". Frequently Given Answers. http://homepage.ntlworld.com./jonathan.deboynepollard/FGA/google-result-counts-are-a-meaningless-metric.html. 
7. browser diversity jQuery
8. FITML Antonio Gulli and Alessio Signorini (2005-08-29). The Indexable Web is more than 11.5 billion pages. web. 
9. touchscreen Alvin More and Brian H. Murray (2000) (PDF). Sizing the Internet. Cyveillance Inc.. 
10. ^ Mark Liberman (2009), "Quotes with and without quotes", Language Log.
11. input transformation Mark Liberman (2005), "we love the web", Language Log, and other Language Log posts linked from there.

Further reading

• Joe Meert (2006-04-30). web app. Science, AntiScience and Geology. web app. —Meert observes that "The temptation to find a quick retort means that, many times, people don't bother to check the source carefully." and that "people will look for a specific phrase that may be taken out-of-context to support their argument". He states that it is "dangerous and irresponsible to think that we can Google away a complex discussion" and that he has "learned long ago that there is no substitute for detailed research on a topic".
• Rich Turner (2004-02-29). keyboard. Grumbles. web app. —Turner points out that "that something gets hits on Google does not make it correct" and gives several examples of things that are incorrect that garner thousands of hits on Google search results.
• Thelwall, M. (2008). Quantitative comparisons of search engine results, Journal of the American Society for Information Science and Technology, 59(11), 1702–1710. http://www.scit.wlv.ac.uk/~cm1993/papers/SearchEngineComparisons_preprint.doc
• Thelwall, M. (2008). Extracting accurate and complete results from search engines: Case study Windows Live. Journal of the American Society for Information Science and Technology, 59(1), 38–50. http://www.scit.wlv.ac.uk/~cm1993/papers/2007_Accurate_Complete_preprint.doc
• Gomes, et al. (2000). Detecting query-specific duplicate documents. http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=1&p=1&f=G&l=50&d=PTXT&S1=6615209.PN.&OS=pn/6615209&RS=PN/6615209
• Thelwall, M. (2008). Quantitative comparisons of search engine results, Journal of the American Society for Information Science and Technology, 59(11), 1702–1710. FITML
• Nakov, Preslav and Hearst, Marti (2005). A Study of Using Search Engine Page Hits as a Proxy for n-gram Frequencies, Proceedings of Recent Advances in Natural Language Processing 2005 http://biotext.berkeley.edu/papers/nakov_ranlp2005.pdf
• Baroni, Marco and Ueyama, Motoko (2006) Building general- and special-purpose corpora by Web crawling, Proceedings of the 13th NIJL International Symposium Language Corpora Their Compilation and Application. device database

See also

• input transformation
• jQuery, a way to filter sites from Google search to remove sites which mirror Wikimedia content
• {{Find sources}}, a template designed to help with Google Books, News archive and Scholar searches