%0 Book Section %1 sureka2010mining %A Sureka, Ashish %A Kumaraguru, Ponnurangam %A Goyal, Atul %A Chhabra, Sidharth %B Information Retrieval Technology %D 2010 %E Cheng, Pu-Jen %E Kan, Min-Yen %E Lam, Wai %E Nakov, Preslav %I Springer Berlin Heidelberg %K socialMedia youtube %P 13-24 %R 10.1007/978-3-642-17187-1_2 %T Mining YouTube to Discover Extremist Videos, Users and Hidden Communities %U http://dx.doi.org/10.1007/978-3-642-17187-1_2 %V 6458 %X We describe a semi-automated system to assist law enforcement and intelligence agencies dealing with cyber-crime related to promotion of hate and radicalization on the Internet. The focus of this work is on mining YouTube to discover hate videos, users and virtual hidden communities. Finding precise information on YouTube is a challenging task because of the huge size of the YouTube repository and a large subscriber base. We present a solution based on data mining and social network analysis (using a variety of relationships such as friends, subscriptions, favorites and related videos) to aid an analyst in discovering insightful and actionable information. Furthermore, we performed a systematic study of the features and properties of the data and hidden social networks which has implications in understanding extremism on Internet. We take a case study based approach and perform empirical validation of the proposed hypothesis. Our approach succeeded in finding hate videos which were validated manually. %@ 978-3-642-17186-4

@incollection{sureka2010mining, abstract = {We describe a semi-automated system to assist law enforcement and intelligence agencies dealing with cyber-crime related to promotion of hate and radicalization on the Internet. The focus of this work is on mining YouTube to discover hate videos, users and virtual hidden communities. Finding precise information on YouTube is a challenging task because of the huge size of the YouTube repository and a large subscriber base. We present a solution based on data mining and social network analysis (using a variety of relationships such as friends, subscriptions, favorites and related videos) to aid an analyst in discovering insightful and actionable information. Furthermore, we performed a systematic study of the features and properties of the data and hidden social networks which has implications in understanding extremism on Internet. We take a case study based approach and perform empirical validation of the proposed hypothesis. Our approach succeeded in finding hate videos which were validated manually.}, added-at = {2013-01-15T16:27:56.000+0100}, author = {Sureka, Ashish and Kumaraguru, Ponnurangam and Goyal, Atul and Chhabra, Sidharth}, biburl = {https://www.bibsonomy.org/bibtex/26c9e689b0086223afd9e27e68da04a4a/asmelash}, booktitle = {Information Retrieval Technology}, description = {Mining YouTube to Discover Extremist Videos, Users and Hidden Communities - Springer}, doi = {10.1007/978-3-642-17187-1_2}, editor = {Cheng, Pu-Jen and Kan, Min-Yen and Lam, Wai and Nakov, Preslav}, interhash = {856a3ac0e662081e844e998647bce9f1}, intrahash = {6c9e689b0086223afd9e27e68da04a4a}, isbn = {978-3-642-17186-4}, keywords = {socialMedia youtube}, pages = {13-24}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2013-01-15T16:27:56.000+0100}, title = {Mining YouTube to Discover Extremist Videos, Users and Hidden Communities}, url = {http://dx.doi.org/10.1007/978-3-642-17187-1_2}, volume = 6458, year = 2010 }