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Michael L. Nelson
Researcher at Old Dominion University
Publications - 430
Citations - 9042
Michael L. Nelson is an academic researcher from Old Dominion University. The author has contributed to research in topics: Web page & Digital library. The author has an hindex of 43, co-authored 388 publications receiving 8354 citations. Previous affiliations of Michael L. Nelson include Langley Research Center & University of Oklahoma.
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An Evaluation of Link Neighborhood Lexical Signatures to Rediscover Missing Web Pages
TL;DR: This work demonstrates a system of constructing a lexical signature for a page from its link neighborhood, that is the "backlinks", or pages that link to the missing page using only ten backlink pages.
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ResourceSync: Leveraging Sitemaps for Resource Synchronization
Bernhard Haslhofer,Simeon Warner,Carl Lagoze,Martin Klein,Robert Sanderson,Michael L. Nelson,Herbert Van de Sompel +6 more
TL;DR: ResourceSync as discussed by the authors is a general Web resource synchronization protocol that leverages XML Sitemaps and provides a set of capabilities that can be combined in a modular manner to meet local or community requirements.
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Supporting Web Archiving via Web Packaging
TL;DR: It is shown that Web Packaging has significant potential to help address challenges related to web archiving, replaying archived web resources, and verifying their authenticity and areas in which changes are needed in order to fully realize that potential.
Off-Topic Memento Toolkit.
TL;DR: The Off-Topic Memento Toolkit is presented, which allows users to detect off-topic mementos within web archive collections and establishes a default threshold corresponding to the best F1 score for each measure.
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365 Dots in 2019: Quantifying Attention of News Sources.
TL;DR: The overlap of topics of online news articles from a variety of sources is investigated by measuring this overlap and scoring news stories according to the degree of attention in near-real time to enable multiple studies, including identifying topics that receive the most attention from news organizations and identifying slow news days versus major news days.