Who and what links to the Internet Archive

AlNoamany, Yasmin; AlSum, Ahmed; Weigle, Michele C.; Nelson, Michael L.

doi:10.1007/s00799-014-0111-5

Cited by 30 publications

(14 citation statements)

References 36 publications

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“…However, in this work, we assume that the requested web page is neither available in web archives nor on the live web and thus is considered to be a "lost" web page. This assumption reflects previous work showing that users often search web archives when they cannot find the desired web page on the live web [5] and that there are a significant number of web pages that are not archived [1,3]. Learning about a requested web page without examining the content of the page can be challenging due to little context and content available.…”

Section: Introductionmentioning

confidence: 56%

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Making Recommendations from Web Archives for "Lost" Web Pages

Alkwai

Nelson

Weigle

2020

Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020

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When a user requests a web page from a web archive, the user will typically either get an HTTP 200 if the page is available, or an HTTP 404 if the web page has not been archived. This is because web archives are typically accessed by Uniform Resource Identifier (URI) lookup, and the response is binary: the archive either has the page or it does not, and the user will not know of other archived web pages that exist and are potentially similar to the requested web page. In this paper, we propose augmenting these binary responses with a model for selecting and ranking recommended web pages in a Web archive. This is to enhance both HTTP 404 responses and HTTP 200 responses by surfacing web pages in the archive that the user may not know existed. First, we check if the URI is already classified in DMOZ or Wikipedia. If the requested URI is not found, we use machine learning to classify the URI using DMOZ as our ontology and collect candidate URIs to recommended to the user. The classification is in two parts, a first-level classification and a deep classification. Next, we filter the candidates based on if they are present in the archive. Finally, we rank candidates based on several features, such as archival quality, web page popularity, temporal similarity, and URI similarity. We calculated the F 1 score for different methods of classifying the requested web page at the first level. We found that using all-grams from the URI after removing numerals and the top-level domain (TLD) produced the best result with F 1 =0.59. For the deep-level classification, we measured the accuracy at each classification level. For second-level classification, the micro-average F 1 =0.30 and for third-level classification, F 1 =0.15. We also found that 44.89% of the correctly classified URIs contained at least one word that exists in a dictionary and 50.07% of the correctly classified URIs contained long strings in the domain. In comparison with the URIs from our Wayback access logs, only 5.39% of those URIs contained only words from a dictionary, and 26.74% contained at least one word from a dictionary. These percentages are low and may affect the ability for the requested URI to be correctly classified.

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Section: Introductionmentioning

confidence: 56%

“…The stop words were based on a set of stop words in the Natural Language Toolkit (NLTK) 5 . We found that using the all-grams from the URI after removing the TLD and numbers had the highest F 1 score, which was comparable to results obtained in related work [25].…”

Section: Step 1: First-level Classificationmentioning

confidence: 99%

Making Recommendations from Web Archives for "Lost" Web Pages

Alkwai

Nelson

Weigle

2020

Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020

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“…Archival materials were obtained from the Internet Archive (IA). The Internet Archive's Wayback Machine is the largest and oldest public Web archive and has become a significant repository of our recent history and cultural heritage [22]. Internet archive resources are provided free of charge to historians, researchers, and for education purposes.…”

Section: Methodsmentioning

confidence: 99%

Hardware Heritage—Briefcase-Sized Computers

Król

2021

Heritage

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The computer industry was a vivid place in the 1980s. IT systems and technologies thrived, and the market offered ever better, smaller, and more useful machines. Innovative technical solutions or intelligent designs that satisfied customers’ needs are often listed as computer hardware milestones. Consequently, they became a permanent part of the computerisation history and can be considered hardware heritage artefacts. The purpose of the paper is to analyse the usability of selected portable computer systems. The foundation of the work is a literature review that includes technical specifications, industry reviews, and research papers. Archival materials were obtained from the Internet Archive. Studies have revealed that the main problems design engineers of portable computers had to tackle in the 1980s were the reduction of mass and size of the computer system, portable power (self-power), and the quality of the displayed image.

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“…This search analysis was conducted on the general use of the Portuguese Web Archive (Costa and Silva, 2011). Another study on the Wayback Machine indicates that its users mostly request English pages, followed by pages written in European languages (AlNoamany et al, 2014). Most of these users probably search for pages archived in the Wayback Machine because the requested pages no longer exist on the live Web.…”

Section: Why What and How Do Users Search?mentioning

confidence: 99%

The Past Web

2021

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Table Of Content 0.1 Dedication 0.2 Foreword 0.3 Preface Part 1 The era of information abundance and memory scarcity Chapter 1.0 Part introduction Chapter 1.1 The problem of web ephemera Chapter 1.2 Web archives preserve our digital collective memory Part 2 Collecting before it vanishes Chapter 2.0 Part introduction Chapter 2.1 National web archiving in Australia -representing the comprehensive Chapter 2.2 Web Archiving Singapore: The Realities of National Web Archiving Chapter 2.3 Archiving the social media -the Twitter case Chapter 2.4 Creating Event-Centric Collections from Web ArchivesPart 3 Access methods to analyse the Past web Chapter 3.0 Part introduction Chapter 3.1 Full-text and URL search Chapter 3.2 A holistic view on Web archives Chapter 3.3 Interoperability for Accessing Versions of Web Resources with the Memento Protocol Chapter 3.4 Linking Twitter archives with TV archives Chapter 3.5 Image analytics in web archives Part 4 Researching the past Web Chapter 4.0 Part introduction Chapter 4.1 Digital archaeology in the web of links: reconstructing a late-90s web sphere Chapter 4.2 Quantitative approaches to the Danish web archive Chapter 4.3 Critical Web Archive Research Chapter 4.4 Exploring Online Diasporas: London's French and Latin American Communities in the UK Web Archive Chapter 4.5 Platform and app histories: Assessing source availability in web archives and app repositories Part 5 Web archives as infrastructures to develop innovative tools Chapter 5.0 Part introduction Chapter 5.1 The need for infrastructures for the study of web-archived material Chapter 5.2 Automatic generation of timelines for past events Chapter 5.3 Political opinions of the past Web Chapter 5.4 Framing web archives with browsers contemporary to a website's creation Chapter 5.5 Big Data analytics over past web data Part 6 The Past Web: a look into the future.This book is dedicated to Vitalino Gomes who taught me that the value of a Man is in his Integrity.

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Who and what links to the Internet Archive

Cited by 30 publications

References 36 publications

Making Recommendations from Web Archives for "Lost" Web Pages

Making Recommendations from Web Archives for "Lost" Web Pages

Hardware Heritage—Briefcase-Sized Computers

The Past Web

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