A large-scale study of the evolution of web pages

TLDR

Web pages change at varying rates, but little is known about how quickly they evolve, how much content is altered, and whether change correlates with page properties, despite prior limited studies such as Cho and Garcia‑Molina’s 720,000‑page crawl. The authors performed a weekly crawl of 150 million HTML pages over 11 weeks, recording checksums, word vectors, page length, HTTP status, and for 0.1 % of URLs the full text, then analyzed change intensity and its correlation with page attributes. They found that change intensity differs widely across top‑level domains, with larger pages changing more frequently and more drastically than smaller ones, revealing significant variation in web page evolution.

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.