Search Engines vs. SEO Spam: Statistical Methods
Search Engines vs. SEO Spam: Statistical Methods
High placement in a search engine is critical for the success of any online business. Pages appearing higher in the search engine results to queries relevant to a site’s business will get higher targeted traffic. To get this kind of competitive advantage Internet companies employ various SEO techniques in order to optimize certain factors used by search engines to rank results.
In the best case SEO specialists create relevant well-structured keyword rich pages, which not only please the eyes of a search engine crawler but also have value to the human visitor. Unfortunately it takes months for this strategic approach to produce feasible results, and many search engine optimizers use so-called “black-hat” SEO.
‘Black Hat’ SEO and Search Engine Spam
The oldest and simplest “black SEO” strategy is adding a variety of popular keywords into web pages to make them rank high for popular queries. This behavior is easily detected since generally such pages include unrelated keywords that lack topical focus. With the introduction of the term vector analysis search engine became immune to this sort of manipulation. However “black-hat’ SEO went one step further creating the so-called “doorway’ pages – tightly focused pages consisting of a bunch of keywords relevant to a single topic. In terms of keyword density such pages are able to rank high in search results but never seen by human visitors as they are redirected to the page intended to receive the traffic.
Another trend is the abusing the link popularity based ranking algorithms, such as PageRank with the help of dynamically-generated pages. Such pages receive the minimum guaranteed PageRank and the small endorsements from thousands of these pages are able to produce a sizeable PageRank for the target page. Search engines constantly improve their algorithms trying to minimize the effect of “black-hat”‘ SEO techniques, but SEOs also persistently respond with new more sophisticated and technically advanced tricks so that this process bears a resemblance to an arms race.
“Black-hat” SEO is responsible for the immense amount of search engine spam-pages and links created solely to mislead search engines and boost rankings for client web sites. To weed out the web spam search engines can use statistical methods that allow computing distributions for a variety of page properties. The outlier values in these distributions can be associated with web spam. The ability to identify web spam is extremely valuable to search engine not just because it allows excluding spam pages from their indices but also using them to train more sophisticated machine learning algorithms capable to battle web spam with higher precision.
Using Statistics to Detect Search Engine Spam
An example of an application of statistical methods to detect web spam is presented in the paper “Spam, Damn Spam and Statistics” by Dennis Fetterly, Mark Manasse and Marc Najork from Microsoft. They used two sets of pages downloaded from the Internet. The first set was crawled repeatedly from November 2002 to February 2003 and consisted from 150 million URLs. For each page the researches recorded HTTP status, time of download, document length, number of non-markup words, and a vector indicating the changes in page content between downloads. A sample of this set (751 pages) was inspected manually and 61 spam pages were discovered, or 8.1% of the set with a confidence interval of 1.95% at 95% confidence.
Another set was crawled between July and September 2002 and comprises 429 million pages and 38 million HTTP redirects. For this set the following properties were recorded: URL, URLs of outgoing links; for the HTTP redirects – the source and the target URL. 535 pages were manually inspected and 37 of them were identified as spam (6.9%).
The research concentrates on studying the following properties of web pages:
– URL properties, including length and percentage of non-alphabetical characters (dashes, digits, dots etc.).
– Host name resolutions.
– Linkage properties.
– Content properties.
– Content evolution properties.
– Clustering properties.
URL Properties
Search engine optimizers often use numerous automatically generated pages to massively distribute their low PageRank to a single target page. Since the pages are machine generated we can expect their URLs to look differently from those created by humans. The assumptions are that these URLs are longer and include more non-alphabetical characters such as dashes, slashes or digits. When searching for spam pages we should consider the host component only, not the entire URL down to the page name.
The manual inspection of the 100 longest hostnames had revealed that 80 of them belong to adult site and 11 refer to the financial and credit related sites. Therefore in order to produce a spam identification rule the length property has to be combined with the