The weaponization of web archives: Data craft and COVID-19 publics

Author:

Acker Amelia1,Chaiet Mitch2

Affiliation:

1. School of Information, University of Texas, USA

2. Moody School of Communication, University of Texas, USA

Abstract

An unprecedented volume of harmful health misinformation linked to the coronavirus pandemic has led to the appearance of misinformation tactics that leverage web archives in order to evade content moderation on social media platforms. Here we present newly identified manipulation techniques designed to maximize the value, longevity, and spread of harmful and non-factual content across social media using provenance information from web archives and social media analytics. After identifying conspiracy content that has been archived by human actors with the Wayback Machine, we report on user patterns of “screensampling,” where images of archived misinformation are spread via social platforms. We argue that archived web resources from the Internet Archive’s Wayback Machine and subsequent screenshots contribute to the COVID-19 “misinfodemic” in platforms. Understanding these manipulation tactics that use sources from web archives reveals something vexing about information practices during pandemics—the desire to access reliable information even after it has been moderated and fact-checked, for some individuals, will give health misinformation and conspiracy theories more traction because it has been labeled as specious content by platforms.

Funder

Institute of Museum and Library Services

Publisher

Shorenstein Center for Media, Politics, and Public Policy

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Challenges in replaying archived Twitter pages;International Journal on Digital Libraries;2023-08-26

2. Know(ing) Infrastructure: The Wayback Machine as object and instrument of digital research;Convergence: The International Journal of Research into New Media Technologies;2023-03-30

3. Forensic Analysis of Memetic Image Propagation: Introducing the SMOC BRISQUEt Method;Proceedings of the Association for Information Science and Technology;2021-10

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