The Extreme Right Filter Bubble

Due to its status as the most popular video sharing platform, YouTube plays an important role in the online strategy of extreme right groups, where it is often used to host associated content such as music and other propaganda. In this paper, we develop a categorization suitable for the analysis of extreme right channels found on YouTube. By combining this with an NMF-based topic modelling method, we categorize channels originating from links propagated by extreme right Twitter accounts. This method is also used to categorize related channels, which are determined using results returned by YouTube’s related video service. We identify the existence of a \lter bubble", whereby users who access an extreme right YouTube video are highly likely to be recommended further extreme right content.

[1]  Vincent Simonet.  Classifying YouTube channels: a practical system , 2013, WWW '13 Companion.

[2]  Lixin Gao,et al.  The impact of YouTube recommendation system on video views , 2010, IMC '10.

[3]  Padhraic Smyth,et al.  Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model , 2006, NIPS.

[4]  Kathleen M. Blee,et al.  Conservative and Right-Wing Movements , 2010 .

[5]  Shankar Kumar,et al.  Video suggestion and discovery for youtube: taking random walks through the view graph , 2008, WWW.

[6]  S Hanley,et al.  Populist Radical Right Parties in Europe. , 2008 .

[7]  T. Brown.  Subcultures, Pop Music and Politics: Skinheads and "Nazi Rock" in England and Germany , 2004 .

[8]  Flavio Figueiredo,et al.  The tube over time: characterizing popularity growth of youtube videos , 2011, WSDM '11.

[9]  Derek Greene,et al.  Uncovering the wider structure of extreme right communities spanning popular online networks , 2013, WebSci.

[10]  Eli Pariser,et al.  The Filter Bubble: What the Internet Is Hiding from You , 2011 .

[11]  Yu He,et al.  The YouTube video recommendation system , 2010, RecSys '10.

[12]  Tao Mei,et al.  SocialTransfer: cross-domain transfer learning from social streams for media applications , 2012, ACM Multimedia.

[13]  Maura Conway,et al.  Jihadi Video and Auto-radicalisation: Evidence from an Exploratory YouTube Study , 2008, EuroISI.

[14]  Keith B. Hall,et al.  Improved video categorization from text metadata and user comments , 2011, SIGIR '11.

[15]  Luca Tateo,et al.  The Italian Extreme Right On-line Network: An Exploratory Study Using an Integrated Social Network Analysis and Content Analysis Approach , 2006, J. Comput. Mediat. Commun..

[16]  Matthew J. Goodwin,et al.  The Roots of Extremism: The English Defence League and the Counter-Jihad Challenge , 2013 .

[17]  Derek Greene,et al.  Producing a unified graph representation from multiple social network views , 2013, WebSci.

[18]  Matthew J. Goodwin,et al.  The New Radical Right: Violent and Non-Violent Movements in Europe , 2012 .

[19]  Derek Greene,et al.  An Analysis of Interactions Within and Between Extreme Right Communities in Social Media , 2012, MSM/MUSE.

[20]  Christos Boutsidis,et al.  SVD based initialization: A head start for nonnegative matrix factorization , 2008, Pattern Recognit..

[21]  Phyllis B. Gerstenfeld,et al.  Hate Online: A Content Analysis of Extremist Internet Sites , 2003 .

[22]  Chih-Jen Lin,et al.  Projected Gradient Methods for Nonnegative Matrix Factorization , 2007, Neural Computation.

[23]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[24]  Jamie Bartlett,et al.  The New Face of Digital Populism , 2011 .

[25]  P. Marlière.  Populism and the enchanted world of moderate politics , 2013 .

[26]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[27]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[28]  Claudius Wagemann,et al.  ONLINE NETWORKS OF THE ITALIAN AND GERMAN EXTREME RIGHT , 2009 .

[29]  Melissa Lamont,et al.  Southern poverty law center , 1997 .

[30]  V. Burris,et al.  White Supremacist Networks on the Internet , 2000 .

[31]  John Hannon,et al.  Recommending twitter users to follow using content and collaborative filtering approaches , 2010, RecSys '10.