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Reproducibility and replicability of web measurement studies

Abstract: Web measurement studies can shed light on not yet fully understood phenomena and thus are essential for analyzing how the modern Web works. This often requires building new and adjusting existing crawling setups, which has led to a wide variety of analysis tools for different (but related) aspects. If these efforts are not sufficiently documented, the reproducibility and replicability of the measurements may suffer---two properties that are crucial to sustainable research. In this paper, we survey 117 recent research papers to derive best practices for Web-based measurement studies and specify criteria that need to be met in practice. When applying these criteria to the surveyed papers, we find that the experimental setup and other aspects essential to reproducing and replicating results are often missing. We underline the criticality of this finding by performing a large-scale Web measurement study on4.5 million pages with 24 different measurement setups to demonstrate the influence of the individual criteria. Our experiments show that slight differences in the experimental setup directly affect the overall results and must be documented accurately and carefully. TechnicalRemarks: This dataset holds additional material to the paper "Reproducibility and Replicability of Web Measurement Studies" submitted to the ACM Web Conference 2022. It contains the measurement data (requests, responses, visited URLs, cookies, and LocalStorage objects) we have collected from 25 different profiles. All data is in CSV format (exported from the Google BigQuery service) and can be imported into any database.

Table sizes (according to Google BigQuery):

Cookies: 2.8 GB LocalStorage: 6 GB Requests: 626.6 GB Responses: 501.6 GB URL: 38 MB Visits: 935 MB

Note: Although our paper does not include the analysis for the collected Cookie and LocalStorage objects, we publish them for further studies.

You can find further information about our study on our repository in GitHub.

Cite this as

Demir, Nurullah, Große-Kampmann, Matteo, Urban, Tobias, Wressnegger, Christian, Holz, Thorsten, Pohlmann, Norbert (2023). Dataset: Reproducibility and replicability of web measurement studies. https://doi.org/10.35097/1560

DOI retrieved: 2023

Additional Info

Field Value
Imported on August 4, 2023
Last update August 4, 2023
License CC BY 4.0 Attribution
Source https://doi.org/10.35097/1560
Author Demir, Nurullah
More Authors
Große-Kampmann, Matteo
Urban, Tobias
Wressnegger, Christian
Holz, Thorsten
Pohlmann, Norbert
Source Creation 2023
Publishers
Karlsruhe Institute of Technology
Production Year 2021
Publication Year 2023
Subject Areas
Name: Computer Science