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Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems
Date Issued
2015
Date Available
2017-04-19T14:42:58Z
Abstract
In addition to hosting user-generated video content, YouTube provides recommendation services,where sets of related and recommended videos are presented to users, based on factors such as covisitation count and prior viewing history. This article is specifically concerned with extreme right(ER) video content, portions of which contravene hate laws and are thus illegal in certain countries,which are recommended by YouTube to some users. We develop a categorization of this content based on various schema found in a selection of academic literature on the ER, which is then used to demonstrate the political articulations of YouTubes recommender system, particularly the narrowing of the range of content to which users are exposed and the potential impacts of this. For this purpose, we use two data sets of English and German language ER YouTube channels, along with channels suggested by YouTubes related video service. A process is observable whereby users accessing an ER YouTube video are likely to be recommended further ER content, leading to immersion in an ideological bubble in just a few short clicks. The evidence presented in this article supportsa shift of the almost exclusive focus on users as content creators and protagonists in extremist cyberspaces to also consider online platform providers as important actors in these same spaces.
Sponsorship
Science Foundation Ireland
Other Sponsorship
2CENTRE
EU funded Cybercrime Centres of Excellence Network
EU funded VOX-Pol Network of Excellence
Type of Material
Journal Article
Publisher
Sage Publications
Journal
Social Science Computer Review
Volume
33
Issue
4
Start Page
459
End Page
478
Copyright (Published Version)
2014 the Authors
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
File(s)
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Name
Down_the_Rabbit_Hole_Preprint_Version.pdf
Size
795.38 KB
Format
Adobe PDF
Checksum (MD5)
9b06ee45406cc5b27ceed7287e78ac3c
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