Estimating search engine index size variability: a 9-year longitudinal study

TLDR

The size of a search engine’s index is a key determinant of search quality and reveals which parts of the Web are directly accessible to users. The study proposes a novel method to estimate a search engine’s index size by extrapolating from word document frequencies in a large static Web corpus. The method uses document frequencies from a large static corpus and applies them to Google and Bing indices over a nine‑year period (March 2006–January 2015) to provide a longitudinal view. Index size estimates for Google and Bing vary dramatically over time, with Google usually larger; this variability is largely attributable to changes in their indexing and ranking infrastructure, undermining the reliability of one‑off estimates and cross‑sectional webometric studies.

Abstract

One of the determining factors of the quality of Web search engines is the size of their index. In addition to its influence on search result quality, the size of the indexed Web can also tell us something about which parts of the WWW are directly accessible to the everyday user. We propose a novel method of estimating the size of a Web search engine’s index by extrapolating from document frequencies of words observed in a large static corpus of Web pages. In addition, we provide a unique longitudinal perspective on the size of Google and Bing’s indices over a nine-year period, from March 2006 until January 2015. We find that index size estimates of these two search engines tend to vary dramatically over time, with Google generally possessing a larger index than Bing. This result raises doubts about the reliability of previous one-off estimates of the size of the indexed Web. We find that much, if not all of this variability can be explained by changes in the indexing and ranking infrastructure of Google and Bing. This casts further doubt on whether Web search engines can be used reliably for cross-sectional webometric studies.