Modeling Updates of Scholarly Webpages Using Archived Data

TL;DR

This paper presents a method to model webpage update dynamics using archived data, improving crawl strategies for scholarly web pages by estimating update frequencies with limited resources.

Contribution

It introduces a novel approach to estimate webpage update frequencies from archived data, enhancing crawl efficiency for scholarly web content.

Findings

Archived data can accurately estimate short-term update frequencies.

The proposed method outperforms baseline models in resource efficiency.

Optimized crawling strategies improve coverage and freshness of scholarly web pages.

Abstract

The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources. Crawl frontiers thus need to be optimized to improve the coverage and freshness of crawled content. In this paper, we propose an approach for modeling the dynamics of change in the web using archived copies of webpages. To evaluate its utility, we conduct a preliminary study on the scholarly web using 19,977 seed URLs of authors' homepages obtained from their Google Scholar profiles. We first obtain archived copies of these webpages from the Internet Archive (IA), and estimate when their actual updates occurred. Next, we apply maximum likelihood to estimate their mean update frequency ($\lambda$) values. Our evaluation shows that $\lambda$ values derived from a short history of archived data provide a good estimate for the true update frequency in the short-term, and that our method provides better estimations of updates at a fraction of resources compared to the baseline models. Based on this, we demonstrate the utility of archived data to optimize the crawling strategy of web crawlers, and uncover important challenges that inspire future research directions.