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, DerekGreene, DerekConway, MauraCarthy, JoeCunningham, Pádraig
Permanent link: http://hdl.handle.net/10197/8439
Date: 2015
Online since: 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.
Funding Details: Science Foundation Ireland
Funding Details: 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
Keywords: Recommender systemsExtreme rightCategorizationRecommender systemsTopic modelingYouTube
DOI: 10.1177/0894439314555329
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

Show full item record

SCOPUSTM   
Citations 10

27
Last Week
1
Last month
checked on Sep 11, 2020

Page view(s)

957
Last Week
4
Last month
13
checked on Apr 11, 2021

Download(s) 50

290
checked on Apr 11, 2021

Google ScholarTM

Check

Altmetric


If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.