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N-Gram Assisted Youtube Spam Comment Detection

  • Published:2018 Issue: Volume:132 Page:174-182
  • ISSN:1877-0509
  • Container-title:Procedia Computer Science
  • language:en
  • Short-container-title:Procedia Computer Science

Author:

Aiyar Shreyas,Shetty Nisha P

Publisher

Elsevier BV

Subject

General Engineering

Reference20 articles.

1. Youtube, Youtube Press Statistics, https://www.youtube.com/yt/about/press/.

2. D. O’Callaghan, M. Harrigan, J. Carthy, P. Cunningham, Network analysis of recurring youtube spam campaigns, CoRR abs/1201.3783.

3. BBC, Glitch in YouTube’s tool for tracking obscene comments), http://www.bbc.com/news/blogs-trending-42060357.

4. Youtube, Youtube Moderate & review comments, https://support.google.com/youtube/answer/111870?hl=en.

5. A. Kantchelian, J. Ma, L. Huang, S. Afroz, A. Joseph, J. D. Tygar, Robust detection of comment spam using entropy rate, in: Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence, AISec ‘12, ACM, New York, NY, USA, 2012, pp. 59-70. doi:10.1145/2381896.2381907. URL http://doi.acm.org/10.1145/2381896.2381907.

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1. Youtube Spam Detection Scheme Using Stacked Ensemble Machine Learning Model;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01

2. Spam Detection using Reference Text: A Preliminary Study for Spam Ground Truth Generation;2023-06-30

3. Machine Learning based Spam Comments Detection on YouTube;2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS);2023-05-17

4. Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data;Expert Systems with Applications;2023-05

5. Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets;PeerJ Computer Science;2023-02-08
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