cc_project_author,post_title,cc_project_url,cc_project_category,post_date,keywords,abstract,cc_author_affiliation,cc_class,cc_snippet,cc_dataset_used,cc_derived_dataset_about,cc_derived_dataset_used,cc_derived_dataset_cited
"Xian Gong, Paul X. Mccarthy, Marian-Andrei Rizoiu, Paolo Boldi – University of Technology, Australia; University of New South Wales, Australia; Università degli Studi di Milano, Italy",Harmony in the Australian Domain Space,https://doi.org/10.1145/3614419.3643998,papers,20240101Z00:00:00,,"In this paper we use for the first time a systematic approach in the study of harmonic centrality at a Web domain level, and gather a number of significant new findings about the Australian web. In particular, we explore the relationship between economic diversity at the firm level and the structure of the Web within the Australian domain space, using harmonic centrality as the main structural feature. The distribution of harmonic centrality values is analyzed over time, and we find that the distributions exhibit a consistent pattern across the different years. The observed distribution is well captured by a partition of the domain space into six clusters; the temporal movement of domain names across these six positions yields insights into the Australian Domain Space and exhibits correlations with other non-structural characteristics. From a more global perspective, we find a significant correlation between the median harmonic centrality of all domains in each OECD country and one measure of global trust, the WJP Rule of Law Index. Further investigation demonstrates that 35 countries in OECD share similar harmonic centrality distributions. The observed homogeneity in distribution presents a compelling avenue for exploration, potentially unveiling critical corporate, regional, or national insights.","University of Technology, Australia; University of New South Wales, Australia; Università degli Studi di Milano, Italy",,"There are many public collections of web crawls, but one that is known for being very reliable and quite wide in scope is the Common Crawl1. Common Crawl’s measurements are preferred for web and network analysis due to their extensive coverage, regular updates, and large-scale, publicly accessible datasets, which reduces the need for resource-intensive data collection and is applicable across various research in a reproducible way. [...]",,,,
"Peter Carragher, Evan M. Williams, Kathleen M. Carley – Carnegie Mellon University, USA",Misinformation Resilient Search Rankings with Webgraph-based Interventions,https://doi.org/10.1145/3670410,papers,20240101Z00:00:00,"search engine optimization, misinformation, website reliability, pagerank","The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to reliable domains. We build these interventions on the principles of fairness (penalize sites for what is in their control), generality (label/fact-check agnostic), targeted (increase the cost of adversarial behavior), and scalability (works at webscale). We refine our methods on small-scale webdata as a testbed and then generalize the interventions to a large-scale webgraph containing 93.9M domains and 1.6B edges. We demonstrate that our methods penalize unreliable domains far more than reliable domains in both settings and we explore multiple avenues to mitigate unintended effects on both the small-scale and large-scale webgraph experiments. These results indicate the potential of our approach to reduce the spread of misinformation and foster a more reliable online information ecosystem. This research contributes to the development of targeted strategies to enhance the trustworthiness and quality of search engine results, ultimately benefiting users and the broader digital community.","Carnegie Mellon University, USA","web-science/hyperlinkgraph, misinformation, disinformation, domain-ranking",,,,,
"Tommaso Fontana, Sebastiano Vigna, Stefano Zacchiroli – Inria, DGDI, Paris, France; Università degli Studi di Milano, Dipartimento di Informatica, Milan, Italy; LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France",WebGraph: The Next Generation (Is in Rust),https://doi.org/10.1145/3589335.3651581,papers,20240101Z00:00:00,,,"Inria, DGDI, Paris, France; Università degli Studi di Milano, Dipartimento di Informatica, Milan, Italy; LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France",web-science/hyperlinkgraph; graph-processing; programming-languages/Java; programming-languages/Rust; cc-cited-not-used,"Moreover, open data projects such as Common Crawl and Software Heritage (SWH) [5] have used WebGraph to compress and distribute their data.",,,,
"{Henry S} Thompson &ndash; The University of Edinburgh, Edinburgh, United Kingdom",Improved methodology for longitudinal Web analytics using Common Crawl,https://www.research.ed.ac.uk/en/publications/improved-methodology-for-longitudinal-web-analytics-using-common-,papers,20240101Z00:00:00,,"Common Crawl is a multi-petabyte longitudinal dataset containing over 100 billion web pages which is widely used as a source of language data for sequence model training and in web science research. Each of its constituent archives is on the order of 75TB in size. Using it for research, particularly longitudinal studies, which necessarily involve multiple archives, is therefore very expensive in terms of compute time and storage space and/or web bandwidth. Two new methods for mitigating this problem are presented here, based on exploiting and extending the much smaller (<200 gigabytes (GB) compressed) index which is available for each archive. By adding Last-Modified timestamps to the index we enable longitudinal exploration using only a single archive. By comparing the distribution of index features for each of the 100 segments into which archive is divided with their distribution over the whole archive, we have identified the least and most representative segments for a number of recent archives. Using this allows the segment(s) that are most representative of an archive to be used as proxies for the whole. We illustrate this approach in an analysis of changes in URI length over time, leading to an unanticipated insight into the how the creation of Web pages has changed over time.","The University of Edinburgh, Edinburgh, United Kingdom","web-archiving, web-dataset",,,,,