Document Level Classifiers and Google Spam Identification

There have been a number of news opinion pieces and blog posts appearing on the Web in recent months telling us that Google has become less useful because of web spam from pages scraping content from other site as well as from low quality articles on content farms. Google’s head of Web Spam, Matt Cutts responded to those criticisms by announcing some new efforts at Google to make those kinds of pages not rank as well in search results. From the Official Google Blog, on January 21, 2011:

As we’ve increased both our size and freshness in recent months, we’ve naturally indexed a lot of good content and some spam as well. To respond to that challenge, we recently launched a redesigned document-level classifier that makes it harder for spammy on-page content to rank highly.

The new classifier is better at detecting spam on individual web pages, e.g., repeated spammy words—the sort of phrases you tend to see in junky, automated, self-promoting blog comments.

Matt Cutts – Google search and search engine spam

When I got to the section of that post about a “redesigned document-level classifier,” I started asking myself “What might Matt mean by a document level classifier, and how it might work to reduce the amount of spam found in search results.”

To provide a little insight into what a Document Level Classifier is, and how it might work, I dug into Google’s patents to see if I could find an example of a patent that specifically referred to a document level classifier that I may not have written about before.

I found the following patent which uses a document level classifier to understand the language being used on a web page:

Identifying language attributes through probabilistic analysis
Invented by Alexander Franz, Brian Milch, Eric Jackson,Jenny Zhou, and Benjamin Diament
Assignee: Google
US Patent 7,386,438
Granted June 10, 2008
Filed: August 4, 2003


A system and method for identifying language attributes through probabilistic analysis is described. A set of language classes and a plurality of training documents are defined, Each language class identifies a language and a character set encoding. Occurrences of one or more document properties within each training document are evaluated.

For each language class, a probability for the document properties set conditioned on the occurrence of the language class is calculated. Byte occurrences within each training document are evaluated. For each language class, a probability for the byte occurrences conditioned on the occurrence of the language class is calculated.

A Document Level Classifier

A document level classifier is simply a program that might look at a number of attributes it finds upon a page to calculate a probability about a classification for that page. In the case of this patent about language attributes, those attributes could possibly includ things like looking at a character set and language meta tag, like the following:

<head><meta charset=”iso-latin-1″> <META LANG-=”fr”></HEAD>

But, the patent tells us that language and character set meta tags appear upon pages rarely, and are often incorrect when they do.

We’re also told that the search engine could look other clues to identify the language of a page, such as whether or not the domain the page is on uses a specific top level country code. For example, it’s possible that a site on a “.es,” domain is from Spain, and may be in Spanish.

Instead, the approach that this patent takes is to look at features like those, but to also use a text analysis approach that breaks text upon pages into n-grams, or groupings of words that are “n” words long. In this patent, the suggested length is 3 words.

So, if the patent were to look at this page to attempt to classify what language it might be in, it might start at the first sentence of this page, and start breaking it down into n-grams, three words long. So, it might take my first sentence, and start breaking it down into three word phrases like this:

There have been
have been a
been a number
a number of
number of news
of news opinion
news opinion pieces
opinion pieces and

These n-grams might be compared to a number of other pages on the web where the language of those pages is known, to determine that my page (or at least parts of my page) is in English. Note, the Google Books N-gram Viewer runs on a similar body of data as that used in this language detection approach.

The n-gram approach has been used in a number of ways by Google, as noted in the Official Google Research Blog post All Our N-Gram Belong to You:

Here at Google Research we have been using word n-gram models for a variety of R&D projects, such as statistical machine translation, speech recognition, spelling correction, entity detection, information extraction, and others

Using a Document Classifier to Identify Web Spam

A document level classifier doesn’t necessarily have to use an n-gram approach to identify web spam pages, but it’s possible that it might. A Google patent granted this past August included an n-gram approach to classify pages. I wrote about it in How Google Might Fight Web Spam Based upon Classifications and Click Data.

Another post that I wrote about a Google patent which describes how the search engine might identify web spam is Google Patent on Web Spam, Doorway Pages, and Manipulative Articles. That patent lists examples which might indicate that a page is web spam. Those examples include:

  • Whether the text of the document appears to be a normal language or language that might have been generated by a computer. For example, it might contain a large number of keywords, but not any actual sentences.
  • Whether or not the page uses meta tags, and if it does, whether or not those contain a large number of repeated keywords.
  • If there are scripts in the document redirecting visitors to another document upon when they access that page.
  • If the text on the page is the same color as the background of the page
  • Whether the page contains a large number of unrelated links
  • Looking at the history of the document, and whether certain things might have changed, such as the content, the linking structure, and possibly the ownership of the document/
  • The amount of links and anchor text on a page. If there are a lot of links, and very little text that isn’t, that could be a sign that the page is web spam.

Chances are that the redesigned document level classifier that Google is using to try to identify whether or not a page is web spam may look at many of these features as well as others.

What features would you look at if you were designing a document level classifier to identify web spam?


Author: Bill Slawski

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