Estimating Absolute Web Crawl Coverage From Longitudinal Set Intersections

TL;DR

This paper introduces a simple method to estimate the absolute coverage of web crawls using only the archive's own longitudinal data, avoiding external ground truth.

Contribution

It proposes a novel urn process-based approach to measure crawl coverage from URL overlaps in sequential crawls, applicable without external datasets.

Findings

Estimated about 46% coverage for German Academic Web from 2013-2021 crawls.

Method requires no external ground truth and is applicable to any longitudinal focused crawl.

Uses linear regression to infer urn model parameters from crawl data.

Abstract

Web archives preserve portions of the web, but quantifying their completeness remains challenging. Prior approaches have estimated the coverage of a crawl by either comparing the outcomes of multiple crawlers, or by comparing the results of a single crawl to external ground truth datasets. We propose a method to estimate the absolute coverage of a crawl using only the archive's own longitudinal data, i.e., the data collected by multiple subsequent crawls. Our key insight is that coverage can be estimated from the empirical URL overlaps between subsequent crawls, which are in turn well described by a simple urn process. The parameters of the urn model can then be inferred from longitudinal crawl data using linear regression. Applied to our focused crawl configuration of the German Academic Web, with 15 semi-annual crawls between 2013-2021, we find a coverage of approximately 46 percent of the crawlable URL space for the stable crawl configuration regime. Our method is extremely simple, requires no external ground truth, and generalizes to any longitudinal focused crawl.