Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems

Files in This Item:
File Description SizeFormat 
Down_the_Rabbit_Hole_Preprint_Version.pdf795.38 kBAdobe PDFDownload
Title: Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems
Authors: O'Callaghan, Derek
Greene, Derek
Conway, Maura
Carthy, Joe
Cunningham, Pádraig
Permanent link:
Date: 2015
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Sage Publications
Copyright (published version): 2014 the Authors
Keywords: Recommender systemsExtreme rightCategorizationRecommender systemsTopic modelingYouTube
DOI: 10.1177/0894439314555329
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
Insight Research Collection

Show full item record

Citations 50

Last Week
Last month
checked on Aug 18, 2018

Download(s) 50

checked on May 25, 2018

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.