Reproducible Web Corpora: Interactive Archiving with Automatic Quality Assessment

摘要

The evolution of web pages from static HTML pages toward dynamic pieces of software has rendered archiving them increasingly difficult. Nevertheless, an accurate, reproducible web archive is a necessity to ensure the reproducibility of web-based research. Archiving web pages reproducibly, however, is currently not part of best practices for web corpus construction. As a result, and despite the ongoing efforts of other stakeholders to archive the web, tools for the construction of reproducible web corpora are insufficient or ill-fitted. This article presents a new tool tailored to this purpose. It relies on emulating user interactions with a web page while recording all network traffic. The customizable user interactions can be replayed on demand, while requests sent by the archived page are served with the recorded responses. The tool facilitates reproducible user studies, user simulations, and evaluations of algorithms that rely on extracting data from web pages. To evaluate our tool, we conduct the first systematic assessment of reproduction quality for rendered web pages. Using our tool, we create a corpus of 10,000 web pages carefully sampled from the Common Crawl and manually annotated with regard to reproduction quality via crowdsourcing. Based on this data, we test three approaches to automatic reproduction-quality assessment. An off-the-shelf neural network, trained on visual differences between the web page during archiving and reproduction, matches the manual assessments best. This automatic assessment of reproduction quality allows for immediate bugfixing during archiving and continuous development of our tool as the web continues to evolve.