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Leveraging Common Crawl's Longitudinal Web Data: Improved Methodology for Efficient Analytics

Core Concepts
This work presents two new methods to mitigate the high computational cost of conducting longitudinal studies on the large-scale Common Crawl web archive, by exploiting the smaller index data and the segmented structure of the archives.
The paper explores ways to efficiently leverage the Common Crawl dataset, a multi-petabyte longitudinal web archive, for web analytics research. Key highlights: Common Crawl is a valuable but computationally expensive resource for longitudinal web studies, as each archive is on the order of 75TB in size. The authors propose two new methods to reduce the computational burden: Exploiting the much smaller (<200GB) index data available for each archive, which provides metadata for every retrieved web page. Identifying the most representative segments within each archive by comparing the distribution of index features between segments and the whole archive. The authors demonstrate the effectiveness of these methods by analyzing changes in URI length over time, leading to an unexpected insight into the shift from human-authored to machine-generated web content. The paper provides a framework for identifying the best segment proxies for different properties of interest, enabling efficient longitudinal studies on the Common Crawl dataset.
The average size of a compressed Common Crawl archive has grown from 50TB in 2019 to 100TB in 2023. The 2019-35 archive contains 2,955 million successful retrievals, while the 2023-40 archive contains 3,445 million. Around 17% of the successful retrievals in the 2019-35 and 2023-40 archives have a Last-Modified HTTP header.
"Common Crawl is a very-large-scale corpus containing petabytes of data from more than 100 archives. It contains over 100 billion web pages, more than 99% of which are HTML formatted, collected since 2008." "Each Common Crawl archive has six main components: Data files (successful and unsuccessful retrievals), and URI index files."

Deeper Inquiries

How can the insights from this work be applied to other large-scale web datasets beyond Common Crawl?

The methodology and findings from this research on Common Crawl can be applied to other large-scale web datasets to improve longitudinal studies and web analytics. By ranking segments based on their representativeness of the whole archive, researchers can efficiently analyze subsets of data without processing the entire dataset. This approach can help reduce computational costs and storage requirements when working with massive web datasets. Additionally, the use of proxy segments identified through correlation analysis can provide valuable insights into the overall trends and patterns present in the dataset. Researchers can adapt this methodology to other web archives or datasets to streamline data analysis and extract meaningful information from large-scale web data.

What are the potential biases or limitations in using the index data as a proxy for the full web archive content?

While using index data as a proxy for the full web archive content offers advantages in terms of computational efficiency and resource optimization, there are potential biases and limitations to consider. One limitation is the assumption that the distribution of certain features in the index data accurately reflects the distribution in the complete archive. If the index data is not representative of the entire dataset, using it as a proxy may lead to skewed results and inaccurate conclusions. Additionally, the selection of proxy segments based on correlation analysis may introduce bias if certain segments are overrepresented or if the correlation metrics are not robust enough to capture the full complexity of the data. It is essential to validate the representativeness of proxy segments and consider potential biases when using index data as a proxy for full web archive content.

How might the shift from human-authored to machine-generated web content impact the long-term preservation and analysis of web history?

The shift from human-authored to machine-generated web content can have significant implications for the long-term preservation and analysis of web history. Machine-generated content, such as dynamically generated pages or AI-generated text, presents challenges in terms of authenticity, provenance, and interpretability. As more automated processes contribute to web content creation, the traditional methods of web archiving and preservation may need to evolve to capture and retain dynamic and ephemeral content effectively. Analyzing machine-generated content also requires specialized tools and techniques to differentiate between human-authored and automated content, ensuring the accuracy and reliability of historical web analysis. Researchers and archivists must adapt their preservation strategies and analytical approaches to accommodate the increasing prevalence of machine-generated web content and its impact on the interpretation of web history.