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Classification of YouTube Metadata using Shark Algorithm

by Shubhangi D. Raverkar, Meghana Nagori
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 9
Year of Publication: 2015
Authors: Shubhangi D. Raverkar, Meghana Nagori
10.5120/ijca2015907525

Shubhangi D. Raverkar, Meghana Nagori . Classification of YouTube Metadata using Shark Algorithm. International Journal of Computer Applications. 132, 9 ( December 2015), 18-21. DOI=10.5120/ijca2015907525

@article{ 10.5120/ijca2015907525,
author = { Shubhangi D. Raverkar, Meghana Nagori },
title = { Classification of YouTube Metadata using Shark Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 9 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 18-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number9/23622-2015907525/ },
doi = { 10.5120/ijca2015907525 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:54.040181+05:30
%A Shubhangi D. Raverkar
%A Meghana Nagori
%T Classification of YouTube Metadata using Shark Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 9
%P 18-21
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

YouTube is one of online video sharing platform that contains several videos and users promoting hate and extremism .Because of low barrier to publication and anonymity, YouTube is misused as a platform by most of users and communities to post negative videos spreading hatred against a particular religion, country or person. The problem of finding out of such hatred videos is proposed in this paper. For that there are several tasks: search strategy or algorithm, node similarity computation metric, learning from exemplary poles serving as training data, stopping criterion, node classier and queue manager. There will implementation of: classification algorithm named shark search. There will be comparison of number of words in the language model based comparer, similarity threshold for the classifier and present the results of comparison using standard Information Retrieval metrics such as precision, recall and F-measure. The influential video metadata on YouTube will be studied.[1].

References
  1. Swati Agarwal, Ashish Sureka” A Focused Crawler for Mining Hate and Extremism Promoting Users, Videos and Communities on YouTube”,2014 on “Best –first search and shark search”
  2. Nisha Aggarwal, Swati Agrawal, Ashish Sureka “MiningYouTube Metadata for Detecting Privacy InvadingHarassmentandMisdemeanorVideos”,2014.on“oneclass classifier”.
  3. Vinita Nahar , Xue Li, Chaoyi Pang “An Effective Approach for Cyberbullying Detection” 2013.
  4. Nilesh J.Uke, Dr. Ravindra C. Thool ”Detecting Pornography on Web to Prevent Child Abuse – AComputer Vision Approach ”2013
  5. Ying Chen, Sencun Zhu,Yilu Zhou,Heng Xu “Detecting Offensive Language in Social Media to ProtectAdolescent Online Safety “2013
  6. A. Bermingham, M. Conway, L. McInerney, N. O'Hare, and A. Smeaton. Combining socialnetwork analysis andsentiment analysis to explore thepotential for onlineradicalisation. In Social Network Analysis and Mining, 2009. ASONAM '09.International Conference on Advances in, pages231{236, 2009.)
  7. April Kontostathis,Kelly Reynolds,Andy Garron,Lynne Edwards “Detecting Cyberbullying: Query Terms and Techniques”2013.
  8. Vidushi Chaudhary , Ashish Surekha“Contextual FeatureBased One-Class Classifier Approach for Detecting Video Response Spam onYouTube” (2013)
  9. Christopher C. Yang , Tobun D. Ng”Terrorism and CrimeRelated Weblog Social Network:Link, Content Analysis and Information Visualization(2007)
  10. Dawai Yin , Zhenzhen Xue , Liangjie Liony” Detectionof Harassment on Web 2.0” 2009.
Index Terms

Computer Science
Information Sciences

Keywords

YouTube metadata Social Network Analysis Hate and Extremism Detection online radicalization

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