Using Jaccard Similarity Measure for Detection of Abusive Comments on Video by Indian YouTuber

Karina Bohora¹ , Sanjay Patil²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.3 , pp.24-32, Jun-2021

Online published on Jun 30, 2021

Copyright © Karina Bohora, Sanjay Patil . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In this paper, a Jaccard index based technique for detection and analysis of abusive YouTube comments is proposed. The effectiveness of the designed approach is evaluated using a popular video by one of the most subscribed YouTubers in India having over 24 million subscribers. This entertainment YouTube video titled ‘The Art of Bad Words’ posted by CarryMinati garnered over 25 million views and more than 0.3 million comments within 15 days of being uploaded. Offensive language used in YouTube comments is often culture-specific and hence can be challenging to identify and keep a check on. So, the focus of this study is on comments containing derogatory language prevalent in the Indian subcontinent and thereby violating YouTube’s community guidelines and policies. The approach`s performance is compared for 4 different threshold values of Jaccard coefficient and the impact of each value on the results obtained is illustrated.

Key-Words / Index Term :
Text Mining, YouTube, Data Mining, Online Video, Content Analysis, Jaccard Distance, Popular Video, India

References :
[1] F. Benevenuto, G. Magno, T. Rodrigues, V. Almeida, "Detecting Spammers on Twitter," In Proceedings of CEAS (2010), Vol. 6 No. 12.
[2] B. Liu, M. Hu, J. Cheng, "Opinion Observer: Analyzing and Comparing Opinions on the Web," In Proceedings of International Conference on World Wide Web (WWW-2005), 2005.
[3] A. M. Möller, R. Kühne, S. E. Baumgartner, J. Peter, "Exploring user responses to entertainment and political videos: An automated content analysis of YouTube," Social Science Computer Review, Vol. 37, Issue. 4, pp. 510–528, 2019.
[4] M. Thelwall, P. Sud, F. Vis, "Commenting on YouTube Videos: From Guatemalan Rock to El Big Bang," Journal of the American Society for Information Science and Technology, Vol. 63, Issue 3, pp. 616–629, 2012.
[5] X. Cheng, C. Dale, J. Liu, "Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study," Technical Report arXiv:0707.3670v1 {cs.NI}, Cornell University, arXiv e-prints, July 2007.
[6] P. Schultes, V. Dorner, F. Lehner, "Leave a Comment! An In-Depth Analysis of User Comments on YouTube," Wirtschaftsinformatik Proceedings, 42, 2013.
[7] P. G. Lange, "Commenting on Comments: Investigating Responses to Antagonism on YouTube," Presented at the Society for Applied Anthropology Conference, Tampa, Florida, 2007.
[8] K. Dinakar, R. Reichart, H. Lieberman, "Modeling the Detection of Textual Cyberbullying," In International Conference on Weblog and Social Media - Social Mobile Web Workshop, Barcelona, Spain, 2011.
[9] M. Z. Asghar, S. Ahmad, A. Marwat, F. M. Kundi, "Sentiment Analysis on YouTube: A Brief Survey," MAGNT Research Report, Vol. 3, No. 1, pp. 1250–1257, 2015.
[10] S. Siersdorfer, S. Chelaru, W. Nejdl, "How useful are your comments? Analyzing and predicting YouTube comments and comment ratings," Paper presented at the 19th international conference on World wide web, Raleigh, NC, 2010.
[11] D. Murthy, S. Sharma, "Visualizing YouTube’s comment space: online hostility as a network phenomena," New Media & Society, 2018.
[12] A. J. Murali, V. S. Chooralil, "A Literature Survey on Web-Based Traffic Sentiment Analysis: Methods and Applications," IJSER, Vol. 6, No. 12, pp. 926–930, 2015.
[13] E. Khabiri, J. Caverlee, C. Hsu, "Summarizing User-Contributed Comments," International AAAI Conference on Web and Social Media, North America, July 2011.
[14] E. H. Poche, "Analyzing User Comments On YouTube Coding Tutorial Videos," LSU Master`s Theses, 4452, 2017.
[15] M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, S. Moon, "I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System," Paper presented at IMC’07: Internet Measurement Conference, San Diego, CA, 2007.
[16] A. U. R. Khan, M. Khan, M. B. Khan, "Naïve Multi-label Classification of YouTube Comments Using Comparative Opinion Mining," Procedia Computer Science, 82, pp. 57–64, 2016.
[17] A. Ammari, V. Dimitrova, D. Despotakis, "Semantically Enriched Machine Learning Approach to Filter YouTube Comments for Socially Augmented User Models," UMAP, pp. 71–85, 2011.
[18] D. O’Callaghan, M. Harrigan, J. Carthy, P. Cunningham, "Identifying Discriminating Network Motifs in YouTube Spam," arXiv preprint arXiv:1202.5216, 2012.
[19] M. Hu, B. Liu, "Mining and Summarizing Customer Reviews," Proceedings of the 10th ACM SIGKDD International conference on knowledge discovery and data mining, 2004.
[20] A. Sureka, "Mining User Comment Activity for Detecting Forum Spammers in YouTube," CoRR abs/1103.5044, 2011.
[21] S. Choudhury, J. G. Breslin, "User Sentiment Detection: A YouTube Use Case," Proceedings of The 21st National Conference on Artificial Intelligence and Cognitive Science, 30 August - 1 September, 2010.