Three New Research Papers About Web Archiving, The Internet Archive, and Memento

The three research papers were made available yesterday via arXiv.

Paper #1

Title

Who and What Links to the Internet Archive

Authors

Yasmin AlNoamany, Ahmed AlSum, Michele C. Weigle, Michael L. Nelson
Old Dominion University (All Authors)

Source

via arXiv

Abstract

The Internet Archive’s (IA) Wayback Machine is the largest and oldest public web archive and has become a significant repository of our recent history and cultural heritage. Despite its importance, there has been little research about how it is discovered and used. Based on web access logs, we analyze what users are looking for, why they come to IA, where they come from, and how pages link to IA. We find that users request English pages the most, followed by the European languages. Most human users come to web archives because they do not find the requested pages on the live web. About 65% of the requested archived pages no longer exist on the live web. We find that more than 82% of human sessions connect to the Wayback Machine via referrals from other web sites, while only 15% of robots have referrers. Most of the links (86%) from websites are to individual archived pages at specific points in time, and of those 83% no longer exist on the live web.

Direct to Full Text (12 pages; PDF) 

Paper #2

Title

Access Patterns for Robots and Humans in Web Archives

Authors

Yasmin AlNoamany, Michele C. Weigle, Michael L. Nelson
Old Dominion University (All Authors)

Source

via arXiv
This paper also appears in the proceedings of TPDL 2013
(International Conference on Theory and Practice of Digital Libraries)

Abstract

Although user access patterns on the live web are well-understood, there has been no corresponding study of how users, both humans and robots, access web archives. Based on samples from the Internet Archive’s public Wayback Machine, we propose a set of basic usage patterns: Dip (a single access), Slide (the same page at different archive times), Dive (different pages at approximately the same archive time), and Skim (lists of what pages are archived, i.e., TimeMaps). Robots are limited almost exclusively to Dips and Skims, but human accesses are more varied between all four types. Robots outnumber humans 10:1 in terms of sessions, 5:4 in terms of raw HTTP accesses, and 4:1 in terms of megabytes transferred. Robots almost always access TimeMaps (95% of accesses), but humans predominately access the archived web pages themselves (82% of accesses). In terms of unique archived web pages, there is no overall preference for a particular time, but the recent past (within the last year) shows significant repeat accesses.

Paper #3

Title

Profiling Web Archive Coverage for Top-Level Domain and Content Language

Authors

Ahmed AlSum, Michele C. Weigle, Michael L. Nelson
Old Dominion University
Herbert Van de Sompel
Los Alamos National Laboratory,

Source

via arXiv

Abstract

The Memento aggregator currently polls every known public web archive when serving a request for an archived web page, even though some web archives focus on only specific domains and ignore the others. Similar to query routing in distributed search, we investigate the impact on aggregated Memento TimeMaps (lists of when and where a web page was archived) by only sending queries to archives likely to hold the archived page. We profile twelve public web archives using data from a variety of sources (the web, archives’ access logs, and full-text queries to archives) and discover that only sending queries to the top three web archives (i.e., a 75% reduction in the number of queries) for any request produces the full TimeMaps on 84% of the cases.

Direct to Full Text (12 pages; PDF)
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