commoncrawl_citations_annotated_2020.csv

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
"Joel Mackenzie, Rodger Benham, Matthias Petri, Johanne R. Trippas, J. Shane Culpepper, Alistair Moffat – The University of Melbourne, Melbourne, Australia; RMIT University, Melbourne, Australia; Amazon Alexa, Manhattan Beach, CA, USA",CC-News-En: A large English news corpus,https://doi.org/10.1145/3340531.3412762,papers,20200101Z00:00:00,"corpus, user query variations, collection, news search, crowdsourcing","We describe a static, open-access news corpus using data from the Common Crawl Foundation, who provide free, publicly available web archives, including a continuous crawl of international news articles published in multiple languages. Our derived corpus, CC-News-En, contains 44 million English documents collected between September 2016 and March 2018. The collection is comparable in size with the number of documents typically found in a single shard of a large-scale, distributed search engine, and is four times larger than the news collections previously used in offline information retrieval experiments. To complement the corpus, 173 topics were curated using titles from Reddit threads, forming a temporally representative sampling of relevant news topics over the 583 day collection window. Information needs were then generated using automatic summarization tools to produce textual and audio representations, and used to elicit query variations from crowdworkers, with a total of 10,437 queries collected against the 173 topics. Of these, 10,089 include key-stroke level instrumentation that captures the timings of character insertions and deletions made by the workers while typing their queries. These new resources support a wide variety of experiments, including large-scale efficiency exercises and query auto-completion synthesis, with scope for future addition of relevance judgments to support offline effectiveness experiments and hence batch evaluation campaigns.","The University of Melbourne, Melbourne, Australia; RMIT University, Melbourne, Australia; Amazon Alexa, Manhattan Beach, CA, USA","nlp/text-corpora, nlp/corpus-construction, ir/information-extraction","Our derived corpus, CC-News-En, contains 44 million English documents collected between September 2016 and March 2018. [...] One such example is the CommonCrawl Foundation,[¹ ] who generate large-scale crawls of the web at regular intervals. A key philosophy behind the Common Crawlis to democratize data, allowing open access with no fees. In late 2016, the Common Crawl Foundation announced a news-specific crawl (CC-News), [² ] with documents being added on a daily basis, and covering sources from a wide range of countries and languages. Here we derive a static, English segment of the CC-Newscrawl that we refer to as CC-News-En. Due to the storage and computation costs involved in filtering out non-English documents, we make the complete corpus available as a free resource, along with asuite of tools which can be used to replicate corpus extraction from the original source CC-News data. We also provide a set of 10,437 user query variations over 173 query topics, including keystroke-level data collected from a novel crowdworking experiment. Our goal is to encourage reproducible and replicable experimentation, with greatly reduced barriers to entry. [...] A total of 2,291 CC-News WARC files were processed to build CC-News-En, covering the period 26 August 2016 to 31 March 2018, inclusive. The first and last WARC files inthis collection are as follows: •CC-NEWS-20160826124520-00000.warc.gz •CC-NEWS-20180331191315-00143.warc.gz The resulting subset of compressed WARC files occupies 2.14 TiB of disk space, and contains a total of 102.5 million documents in over 100 languages. [...] Missing Documents and Temporal Gaps. During the creation of the collection, the CC-NEWS-20170812163812-00038.warc.gz file was not processed correctly by our pipeline, and was subsequently dropped from the CC-News-En corpus. In addition, there are six days within the 583 day period where no WARC files were added to the original CC-News crawl: 22/09/2016 – 25/09/2016 inclusive, 18/12/2017, and 22/12/2017. These gaps typically correspond to hardware and software upgrades on the crawl servers.[¹⁸ Private correspondence with Common Crawl Engineers.] It is also important to note that both CC-News and CC-News-En are not intended to be complete crawls of their sources, but rather, to provide a reproducible sample of these sites.",CC-NEWS,,,
"Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, Philipp Koehn – Facebook AI; Johns Hopkins University",CCAligned: A Massive collection of cross-lingual web-document pairs,https://www.aclweb.org/anthology/2020.emnlp-main.480,papers,20200101Z00:00:00,,"Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.",Facebook AI; Johns Hopkins University,"nlp/machine-translation, nlp/text-corpora, nlp/parallel-corpus, nlp/cross-lingual-document-alignment","[...] we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. [...] Starting from 68 Common Crawl snapshots with a raw document count of 169.4 billion documents, upon deduplication, the resultant corpus is approximately 29.6 billion web documents from 107.8 million distinct web domains – a 83{\%} reduction from the raw corpus.",,CCAligned-2020,,
"Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei – Johns Hopkins University; OpenAI",Language models are few-shot learners,https://arxiv.org/abs/2005.14165,papers,20200101Z00:00:00,,,Johns Hopkins University; OpenAI,"nlp/language-model, ai/deep-learning, nlp/autoregressive-transformer-language-model, nlp/question-answering, nlp/machine-translation, nlp/text-generation","Datasets for language models have rapidly expanded, culminating in the Common Crawl dataset [...] constituting nearly a trillion words. [...] However, we have found that unfiltered or lightly filtered versions of Common Crawl tend to have lower quality than more curated datasets. Therefore, we took 3 steps to improve the average quality of our datasets: (1) we downloaded and filtered a version of CommonCrawl based on similarity to a range of high-quality reference corpora, (2) we performed fuzzy deduplication at the document level, within and across datasets, to prevent redundancy and preserve the integrity of our held-out validation set as an accurate measure of overfitting, and (3) we also added known high-quality reference corpora to the training mix to augment CommonCrawl and increase its diversity. Details of the first two points (processing of Common Crawl) are described in Appendix A.",,,,
"Metod Jazbec, Barna Pásztor, Felix Faltings, Nino Antulov-Fantulin, Petter N. Kolm – ETH Zurich, Switzerland; New York University, New York, USA",On the impact of publicly available news and information transfer to financial markets,https://arxiv.org/abs/2010.12002,papers,20200101Z00:00:00,,"We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a nonprofit organization that crawls a large part of the web. We develop a processing pipeline to identify news articles associated with the constituent companies in the S&P 500 index, an equity market index that measures the stock performance of U.S. companies. Using machine learning techniques, we extract sentiment scores from the Common Crawl News data and employ tools from information theory to quantify the information transfer from public news articles to the U.S. stock market. Furthermore, we analyze and quantify the economic significance of the news-based information with a simple sentiment-based portfolio trading strategy. Our findings provides support for that information in publicly available news on the World Wide Web has a statistically and economically significant impact on events in financial markets.","ETH Zurich, Switzerland; New York University, New York, USA","statistical-finance, ai/machine-learning, nlp/sentiment-analysis","In this article, we use news articles from the Common Crawl News, a subset of the Common Crawl’s petabytes of publicly available World Wide Web archives, to measure the impact of the arrival of new information about the constituent stocks in the S&P 500 index at the time of publishing. To the best of our knowledge, our study is the first one to use the Common Crawl in this way. We develop a cloud-based processing pipeline that identifies news articles in the Common Crawl News data that are related to the companies in the S&P 500. As the Common Crawl public data archives are getting bigger, they are opening doors for many real-world “data-hungry” applications such as transformers models GPT49 and BERT50, a recent class of deep learning language models. We believe that public sources of news data is important not only for natural language processing (NLP) and finance communities but also for more general studies in complex systems and computational social sciences that are aiming to characterize (mis)information propagation and dynamics in techno-socio-economic systems. The abundance of high-frequency data around the financial systems enables complex systems researchers to have microscopic observables that allow verification of different models, theories, and hypotheses.",CC-NEWS,,,
"Marco Squarcina, Mauro Tempesta, Lorenzo Veronese, Stefano Calzavara, Matteo Maffei – TU Wien, Austria; Università Ca’ Foscari Venezia, Italy",Can I take your subdomain? Exploring related-domain attacks in the modern web,https://arxiv.org/abs/2012.01946,papers,20200101Z00:00:00,,,"TU Wien, Austria; Università Ca’ Foscari Venezia, Italy","computer-security/internet-security, related-domain attacks","Our web security analysis aims at quantifying the number of domains hosting web applications that can be exploited by taking over the vulnerable domains discovered by RDScan. In particular, for every apex domain with at least one vulnerable subdomain, we selected from the CommonCrawl dataset [¹⁹ Common Crawl. Host- and domain-level webgraphs feb/mar/may 2020. https://commoncrawl.org/2020/06/host-and-domain-level-web-graphs-febmarmay-2020/, 2020.] the list of 200 most popular related-domains according to the Pagerank score [11]. From the homepage of these domains,we extracted the same-origin links that appear in the HTML code.",hyperlinkgraph/cc-main-2020-feb-mar-may/hostgraph,,,
"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu – Google, Mountain View, CA, USA",Exploring the limits of transfer learning with a unified text-to-text transformer,http://jmlr.org/papers/v21/20-074.html,papers,20200101Z00:00:00,,"Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.","Google, Mountain View, CA, USA","nlp/corpus-construction, nlp/language-model","We also introduce our approach for treating every problem as a text-to-text task and describe our “Colossal Clean Crawled Corpus” (C4), the Common Crawl-based data set we created as a source of unlabeled text data. [...] Common Crawl is a publicly-available web archive that provides “web extracted text” by removing markup and other non-text content from the scraped HTML files. This process produces around 20TB of scraped text data each month. Unfortunately, the majority of the resulting text is not natural language. Instead, it largely comprises gibberish or boiler-plate text like menus, error messages, or duplicate text. Furthermore, a good deal of the scraped text contains content that is unlikely to be helpful for any of the tasks we consider (offensive language, placeholder text, source code, etc.). To address these issues, we used the following heuristics for cleaning up Common Crawl’s web extracted text: [...] To assemble our base data set, we downloaded the web extracted text from April 2019 and applied the aforementioned filtering. This produces a collection of text that is not only orders of magnitude larger than most data sets used for pre-training (about 750 GB) but also comprises reasonably clean and natural English text. We dub this data set the “Colossal Clean Crawled Corpus” (or C4 for short) and release it as part of TensorFlow Datasets.⁸ [⁸https://www.tensorflow.org/datasets/catalog/c4]",CC-MAIN-2019-18 (WET),Tensorflow-C4,,
"Jay M. Patel – Specrom Analytics, Ahmedabad, India",Getting structured data from the internet,https://www.apress.com/gp/book/9781484265758,papers,20200101Z00:00:00,,,"Specrom Analytics, Ahmedabad, India",web-mining,[Chapter 6: Introduction to Common Crawl Datasets + Chapter 7: Web Crawl Processing on Big Data Scale],,,,
"Jonathan Dunn – University of Canterbury, Christchurch, New Zealand",Mapping languages: The Corpus of Global Language Use,https://doi.org/10.1007/s10579-020-09489-2,papers,20200101Z00:00:00,,"This paper describes a web-based corpus of global language use with a focus on how this corpus can be used for data-driven language mapping. First, the corpus provides a representation of where national varieties of major languages are used (e.g., English, Arabic, Russian) together with consistently collected data for each variety. Second, the paper evaluates a language identification model that supports more local languages with smaller sample sizes than alternative off-the-shelf models. Improved language identification is essential for moving beyond majority languages. Given the focus on language mapping, the paper analyzes how well this digital language data represents actual populations by (i) systematically comparing the corpus with demographic ground-truth data and (ii) triangulating the corpus with an alternate Twitter-based dataset. In total, the corpus contains 423 billion words representing 148 languages (with over 1 million words from each language) and 158 countries (again with over 1 million words from each country), all distilled from Common Crawl web data. The main contribution of this paper, in addition to describing this publicly-available corpus, is to provide a comprehensive analysis of the relationship between two sources of digital data (the web and Twitter) as well as their connection to underlying populations.","University of Canterbury, Christchurch, New Zealand","nlp/corpus-construction, nlp/language-identification","The raw portions of the Common Crawl dataset used to build the corpus are shown in Table 2. The corpus uses every portion of the crawl from March 2014 to June 2019, totaling 147 billion web pages in total. No temporal divisions are included in the corpus because these dates represent the time of collection rather than the time of production: web data does not expire and there is a long-tail in which the same samples are observed multiple times across different periods.",64 monthly crawls: March 2014 (CC-MAIN-2014-10) -- June 2019 (CC-MAIN-2019-29) (WET),earthlings.io/CGLU,,
"Liang Xu, Xuanwei Zhang, Qianqian Dong – CLUE Organization",CLUECorpus2020: A large-scale Chinese corpus for pre-training language model,https://arxiv.org/abs/2003.01355,papers,20200101Z00:00:00,,,CLUE Organization,nlp/corpus-construction,"we introduce the Chinese corpusfrom CLUE organization, CLUECorpus2020, a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language gen-eration. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl¹. [...] We download the corpus from July to December 2019 from Common Crawl. After the aforementioned filtering method, we extract the corpus of 100GB.",July to December 2019 (WARC),,,
"Andreas Giannakoulopoulos, Minas Pergantis, Nikos Konstantinou, Aristeidis Lamprogeorgos, Laida Limniati, Iraklis Varlamis – Ionian University, Corfu, Greece; Harokopio University of Athens, Athens, Greece",Exploring the Dominance of the English Language on the Websites of EU Countries,http://dx.doi.org/10.3390/fi12040076,papers,20200101Z00:00:00,,"The English language is the most dominant language in the Western world and its influence can be noticed in every aspect of human communication. It’s increasing diffusion, especially since the turn of the century, is hard to measure with conventional means. The present research studies the use of language in websites of European Union (EU) member states, in order to collect data about the prevalence of the English language in the different countries and regions of the European Union.To achieve a realistic representation of today’s landscape of the European Web, this study uses avast population of websites and a representative sampling size and methodology. By analyzing and processing the findings from over 100,000 websites from every country in the EU, a solid foundation is set that is used to explore the dominance of the English language in the European World Wide Web in general. This is the first study that examines the presence of English content in the websites of all EU member countries and provides statistical evidence regarding the ratio of English content availability for each country. Conclusively, the results of the research demonstrate that the English language is available on more than one quarter of all websites of non-English speaking EU member states.Moreover, it is available in the vast majority of multilingual and bilingual websites, while at the same time being the only language that is available in a number of monolingual websites. In addition, it is shown preference over the national language in a significant number of cases. A moderate negative correlation is found between a member state’s population and the availability of English in these countries’ websites and the same holds true for a member state’s Gross Domestic Product (GDP).Both these correlations indicate that smaller countries tend to provide more content in English in order to establish a stronger presence in the international environment. Taking into account the role of language in the expression of national identity, this study provides data and insights which may contribute to the discussion about the changes underway in the national identity of EU member states.","Ionian University, Corfu, Greece; Harokopio University of Athens, Athens, Greece","nlp/corpus-construction, web-science, socio-linguistics","The nature of the present research required as many websites as possible, so that both our total population and our sampling pool were as close a representation of reality as possible. For this purpose,we used information obtained from Common Crawl, a “repository of web crawl data that is universally accessible and analyzable” [34]. Among the data Common Crawl offers is an index of every available webpage for all member states of the EU amongst other countries. A process was developed in PHP:Hypertext Preprocessor (PHP) that used the CompounD indeX (CDX) server Application Program Interface (API) [35] to access Common Crawl’s Uniform Resource Locator (URL) index [36] and created a MariaDB database with information about websites from every member state of the EU. Although Common Crawl’s index provides all available crawled pages, our process of data collecting only focused on recording the landing page of one website per domain.",,,,
"Mukund Srinath, Shomir Wilson, C Lee Giles – Pennsylvania State University, PA, USA",Privacy at scale: Introducing the PrivaSeer corpus of web privacy policies,https://arxiv.org/abs/2004.11131,papers,20200101Z00:00:00,,,"Pennsylvania State University, PA, USA","nlp/corpus-construction, web-science, internet-security/privacy-policies","We used Common Crawl² to gather seed URLs to crawl for privacy policies from the web, as we describe in detail below. We filtered the Common Crawl URLs to get a set of possible links to web site privacy policies. We then crawled the filtered set to obtain candidate privacy policy documents. The complete pipeline from the Common Crawl URL dump to the gold standard privacy policy corpus is shown in Figure 1. [...] The Common Crawl Foundation is a non-profit which has been releasing large monthly internet web crawls since 2008. Monthly crawl archives provide a “snapshot of the web” by including re-crawls of popular domains (re-crawls from previous archives) and crawls of new domains. Common Crawl has also been releasing a domain-level webgraph from which the harmonic centrality of the crawled domains are calculated. This webgraph his used to sample popular domains that need to be re-crawled and to obtain new uncrawled domains. We downloaded the URL dump of the May, 2019 archive. Common Crawl reports that the archive contains 2.65 billion web pages or 220 TB of uncompressed content which were crawled between 19th and 27th of May, 2019. They also report that this archive contains 825 million URLs which were not contained in any previously released crawl archives. We applied a selection criteria on the downloaded URL dump to filter the URLs of likely privacy policy pages.",,,,
"Tianxi Dong, Jason Triche – Trinity University, San Antonio, TX, USA; University of Montana, MT, USA",A longitudinal analysis of job skills for entry-level data analysts,https://jise.org/Volume31/n4/JISEv31n4p312.pdf,papers,20200101Z00:00:00,,,"Trinity University, San Antonio, TX, USA; University of Montana, MT, USA","business-intelligence, nlp/corpus-construction","Our first challenge was how to collect job postings over past years because job websites do not keep historical data for more than one year. Therefore, we used the Common Crawl dataset to address this problem (http://commoncrawl.org/). Common Crawl is a non-profit organization that builds and maintains an open repository of web crawl data that is, in essence, a copy of the Internet. Common Crawl data contains over 25 billion web pages (Batikas, Claussen, and Peukert, 2018) and is widely used in hundreds of research projects (Batikas, Claussen, and Peukert, 2018; Cafarella et al., 2018). Since we were only interested in the content from Indeed.com, we only examined a very small fraction of the Common Crawl corpus.",,,,
"Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, Colin Raffel – Google; Stanford University; UC Berkeley; Northeastern University; OpenAI; Harvard University; Apple",Extracting training data from large language models,https://arxiv.org/abs/2012.07805,papers,20200101Z00:00:00,,,Google; Stanford University; UC Berkeley; Northeastern University; OpenAI; Harvard University; Apple,"ai/ethical-concerns, nlp/language-models","We follow a different data collection process as used in GPT-2 (which follows Reddit links) in order to reduce the likelihood that our dataset has any intersection with the model’s training data. In particular, we select samples from a subset of Common Crawl⁶ [⁶http://commoncrawl.org/] to feed as context to the model.⁷ [⁷It is possible there is some intersection between these two datasets, effectively allowing this strategy to “cheat”. We believe this does not considerably affect results. First, any overlap between the two datasets is rare on average. Second, because we only use the first 5 or 10 tokens of each sample, any possible overlap will be small in absolute terms.]",,,,
"Thaer Sammar, Hadi Khalilia – Palestine Technical University, Tulkarm, West Bank",Going Back in Time to Find What Existed on the Web and How much has been Preserved: How much of Palestinian Web has been Archived?,http://proceedings.sriweb.org/akn/index.php/art/article/view/410,papers,20200101Z00:00:00,,"The web is an important resource for publishing and sharing content. The main characteristic of the web is its volatility. Content is added, updated, and deleted all the time. Therefore, many national and international institutes started crawling and archiving the content of the web. The main focus of national institutes is to archive the web related to their country heritage, for example, the National Library of the Netherlands is focusing on archiving website that are of value to the Dutch heritage. However, there are still countries that haven’t taken the action to archive their web, which will result in loosing and having a gap in the knowledge. In this research, we focus on shedding the light on the Palestinian web. Precisely, how much of the Palestinian web has been archived. First, we create a list of Palestinian hosts that were on the web. For that we queried Google index exploiting the time range filter in order to get hosts overtime. We collected in 98 hosts in average in 5-years granularity from the year 1990 to 2019. We also obtained Palestinian hosts from the DMOZ directory. We collected 188 hosts. Second, we investigate the coverage of collected hosts in the Internet Archive and the Common-Crawl. We found that coverage of Google hosts in the Internet Archive ranges from 0\% to 89\% from oldest to newest time-granularity. The coverage of DMOZ hosts was 96\%. The coverage of Google hosts in the Common-Crawl 57.1\% to 74.3, while the coverage of DMOZ hosts in the Common-Crawl was in average 25\% in all crawls. We found that even the host is covered in Internet Archive and Common-Crawl, the lifespan and the number of archived versions are low.","Palestine Technical University, Tulkarm, West Bank",web-archiving/regional-coverage,,CDX index,,,
"Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, Noah A. Smith – Paul G. Allen School of Computer Science & Engineering, University of Washington, USA; Allen Institute for Artificial Intelligence, Seattle, USA",RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models,https://arxiv.org/abs/2009.11462,papers,20200101Z00:00:00,,,"Paul G. Allen School of Computer Science & Engineering, University of Washington, USA; Allen Institute for Artificial Intelligence, Seattle, USA","no-citation-misclassified, ai/ethics-of-machine-learning, ai/machine-learning, nlp/language-model",,,,,
"Xinyue Wang, Zhiwu Xie – Virginia Polytechnic Institute and State University, Blacksburg, VA, USA",The Case For Alternative Web Archival Formats To Expedite The Data-To-Insight Cycle,https://doi.org/10.1145/3383583.3398542,papers,20200101Z00:00:00,"storage management, big data analysis, web archiving, file format","The WARC file format is widely used by web archives to preserve collected web content for future use. With the rapid growth of web archives and the increasing interest to reuse these archives as big data sources for statistical and analytical research, the speed to turn these data into insights becomes critical. In this paper we show that the WARC format carries significant performance penalties for batch processing workload. We trace the root cause of these penalties to its data structure, encoding, and addressing method. We then run controlled experiments to illustrate how severe these problems can be. Indeed, performance gain of one to two orders of magnitude can be achieved simply by reformatting WARC files into Parquet or Avro formats. While these results do not necessarily constitute an endorsement for Avro or Parquet, the time has come for the web archiving community to consider replacing WARC with more efficient web archival formats.","Virginia Polytechnic Institute and State University, Blacksburg, VA, USA","web-archiving, data formats, big data, data processing, WARC, Parquet",,,,,
"Srdjan Matic, Costas Iordanou, Georgios Smaragdakis, Nikolaos Laoutaris – TU Berlin, Germany; Cyprus University of Technology, Cyprus; IMDEA Networks Institute",Identifying Sensitive URLs at Web-Scale,https://do.tu-berlin.de/handle/11303/13215,papers,20200101Z00:00:00,,"Several data protection laws include special provisions for protecting personal data relating to religion, health, sexual orientation, and other sensitive categories. Having a well-defined list of sensitive categories is sufficient for filing complaints manually, conducting investigations, and prosecuting cases in courts of law. Data protection laws, however, do not define explicitly what type of content falls under each sensitive category. Therefore, it is unclear how to implement proactive measures such as informing users, blocking trackers, and filing complaints automatically when users visit sensitive domains. To empower such use cases we turn to the Curlie.org crowdsourced taxonomy project for drawing training data to build a text classifier for sensitive URLs. We demonstrate that our classifier can identify sensitive URLs with accuracy above 88%, and even recognize specific sensitive categories with accuracy above 90%. We then use our classifier to search for sensitive URLs in a corpus of 1 Billion URLs collected by the Common Crawl project. We identify more than 155 millions sensitive URLs in more than 4 million domains. Despite their sensitive nature, more than 30% of these URLs belong to domains that fail to use HTTPS. Also, in sensitive web pages with third-party cookies, 87% of the third-parties set at least one persistent cookie.","TU Berlin, Germany; Cyprus University of Technology, Cyprus; IMDEA Networks Institute","computer-security/internet-security, privacy, GDPR, general data protection regulation","When it comes to detecting specific sensitive categories, such as those defined by GDPR: Health, Politics, Religion, Sexual Orientation, Ethnicity, our classifier achieves a high classification accuracy as well. For specific categories, such as Health (98%), Politics (92%), Religion (97%), our classifier achieves an accuracy that exceeds the basic classification accuracy between sensitive and non-sensitive URLs (88%).¶ • Applying our classifier on a Common Crawl snapshot of the English speaking Web (around 1 Billion URLs), we identify 155 million sensitive URLs in more than 4 million domains. Health, Religion, and Political Beliefs are the most popular categories with around 70 millions, 35 millions, and 32 millions URLs respectively.¶ • Looking among the identified sensitive URLs we reach the conclusion that sensitive URLs are handled as any other URL, without any special provision for the privacy of users. For example, we show that 30% of sensitive URLs are hosted in domains that fail to use HTTPS. Also, in sensitive web pages with third-party cookies, 87% of the third-parties sets at least one persistent cookie.",,,,
Sebastian Nagel – Common Crawl,Experiments using a Distributed Web Crawler to Process and Index Web Archives,https://doi.org/10.5281/zenodo.4609371,papers,20200101Z00:00:00,,,Common Crawl,"web crawling, web archiving",,,,,
"Sebastian Roth, Timothy Barron, Stefano Calzavara, Nick Nikiforakis, Ben Stock – CISPA Helmholtz Center for Information Security, Germany; Stony Brook University, USA; Università Ca’ Foscari, Venezia, Italy",Complex security policy? a longitudinal analysis of deployed content security policies,https://par.nsf.gov/biblio/10173479,papers,20200101Z00:00:00,,"The Content Security Policy (CSP) mechanism was developed as a mitigation against script injection attacks in 2010. In this paper, we leverage the unique vantage point of the Internet Archive to conduct a historical and longitudinal analysis of how CSP deployment has evolved for a set of 10,000 highly ranked domains. In doing so, we document the long- term struggle site operators face when trying to roll out CSP for content restriction and highlight that even seemingly secure whitelists can be bypassed through expired or typo domains. Next to these new insights, we also shed light on the usage of CSP for other use cases, in particular, TLS enforcement and framing control. Here, we find that CSP can be easily deployed to fit those security scenarios, but both lack wide-spread adoption. Specifically, while the underspecified and thus inconsistently implemented X-Frame-Options header is increasingly used on the Web, CSP’s well-specified and secure alternative cannot keep up. To understand the reasons behind this, we run a notification campaign and subsequent survey, concluding that operators have often experienced the complexity of CSP (and given up), utterly unaware of the easy-to-deploy components of CSP. Hence, we find the complexity of secure, yet functional content restriction gives CSP a bad reputation, resulting in operators not leveraging its potential to secure a site against the non-original attack vectors.","CISPA Helmholtz Center for Information Security, Germany; Stony Brook University, USA; Università Ca’ Foscari, Venezia, Italy","computer-security/internet-security, web-science","To determine this IA-specific influence, we chose a second archive service to corroborate the IA’s data. In particular, Common Crawl (CC) [10] has been collecting snapshots of popular sites since 2013. For each date on which we found a CSP in the IA, we queried the CC API for a matching snapshot. Overall, we found 38,129 overlapping snapshots for 940 sites. Out of these, 729 (1.9%) on 127 sites were inconsistent between the two archives. For 96 cases the difference was the lack of block-all-mixed-content or upgrade-insecure-requests in the CC data. Further investigation showed that in the IA, these directives were separated from the remaining CSP with a comma instead of a semicolon. This likely relates to the IA joining headers with the same name with a comma. For those pages, we could always only find a single CSP header in the CC response. Moreover, starting from August 2018, these sites still used the aforementioned directives in the IA data, but CC returned two CSP headers (one including only those directives). Hence, we speculate this relates to a bug in CC, which was fixed around August 2018.",,,,