A large-scale study of the evolution of web pages

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A large-scale study of the evolution of web pages (2003)

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by Dennis Fetterly , Mark Manasse , Marc Najork , Janet L. Wiener

Venue:	In Proceedings of the 12th International World Wide Web Conference
Citations:	239 - 5 self

BibTeX

@INPROCEEDINGS{Fetterly03alarge-scale,
    author = {Dennis Fetterly and Mark Manasse and Marc Najork and Janet L. Wiener},
    title = {A large-scale study of the evolution of web pages},
    booktitle = {In Proceedings of the 12th International World Wide Web Conference},
    year = {2003},
    pages = {669--678},
    publisher = {ACM Press}
}

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Abstract

How fast does the web change? Does most of the content remain unchanged once it has been authored, or are the documents continuously updated? Do pages change a little or a lot? Is the extent of change correlated to any other property of the page? All of these questions are of interest to those who mine the web, including all the popular search engines, but few studies have been performed to date to answer them. One notable exception is a study by Cho and Garcia-Molina, who crawled a set of 720,000 pages on a daily basis over four months, and counted pages as having changed if their MD5 checksum changed. They found that 40 % of all web pages in their set changed within a week, and 23 % of those pages that fell into the.com domain changed daily. This paper expands on Cho and Garcia-Molina’s study, both in terms of coverage and in terms of sensitivity to change. We crawled a set of 150,836,209 HTML pages once every week, over a span of 11 weeks. For each page, we recorded a checksum of the page, and a feature vector of the words on the page, plus various other data such as the page length, the HTTP status code, etc. Moreover, we pseudo-randomly selected 0.1 % of all of our URLs, and saved the full text of each download of the corresponding pages. After completion of the crawl, we analyzed the degree of change of each page, and investigated which factors are correlated with change intensity. We found that the average degree of change varies widely across top-level domains, and that larger pages change more often and more severely than smaller ones. This paper describes the crawl and the data transformations we performed on the logs, and presents some statistical observations on the degree of change of different classes of pages.

Keyphrases

web page large-scale study change varies http status code popular search engine web change change intensity statistical observation corresponding page average degree full text com domain top-level domain notable exception data transformation garcia-molina study md5 checksum page length various data feature vector different class html page daily basis

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