Sorting URLs out

Seeing the web through infrastructural inversion of archival crawling

Bibliographic Data

ID13119005
AuthorsEmily Maemura (0000-0002-9329-7995, University of Illinois Urbana-Champaign, corresponding author)
Year2023
Volume7
Issue4
Pages386-401
Publication date2023-09-16
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInternet Histories (JOURNAL)
Journal identifiersISSN: 2470-1475 • E-ISSN: 2470-1483
PublisherRoutledge (PUBLISHER • GB)
DOI10.1080/24701475.2023.2258697
OpenAlexW4386802863
LanguageEN
Citations received4
References cited37

Web archives collections have become important sources for Internet scholars by documenting the past versions of web resources. Understanding how these collections are created and curated is of increasing concern and recent web archives scholarship has studied how the artefacts stored in archives represent specific curatorial choices and collecting practices. This paper takes a novel approach in studying web archiving practice, by focusing on the challenges encountered in archival web crawling and what they reveal about the web itself. Inspired by foundational work in infrastructure studies, infrastructural inversion is applied to study how crawler interactions surface otherwise invisible, background or taken-for-granted aspects of the web. This framework is applied to study three examples selected from interviews and ethnographic fieldwork observations of web archiving practices at the Danish Royal Library, with findings demonstrating how the challenges of archival crawling illuminate the web's varied actors, as well as their changing relationships, power differentials and politics. Ultimately, analysis through infrastructural inversion reveals how collection via crawling positions archives as active participants in web infrastructure, both shaping and shaped by the needs and motivations of other web actors

Algorithm · Biology · Crawling · Sorting · Web crawler · World Wide Web · Computer Science · Digital and Cyber Forensics · Digital and Traditional Archives Management · Web Data Mining and Analysis · Anatomy

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