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Content-Based Classification Approach for Video-Spam Identification

  • Palak Agarwal
  • Mahak Sharma
  • Gagandeep Kaur
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

In this paper the authors have worked on YouTube comment spamming. The work has been carried out on a large and labeled dataset of text-comments. Filtration and pre-processing was done to speed up the detection, elimination of redundancies as well as to increase the accuracy. Spam flags on each set of text-comments were used to check the accuracy in implementation of classification techniques. An improved algorithm has also been proposed based on term frequencies. The results were compared based on accuracy-score and F-score considering the spam flag corresponding to each comment. Further, the accuracy of SVM model was compared with respect to size of dataset, pre-processing of data as well as with XGBoost.

Keywords

Spam/Ham XGBoost TF-IDF RCA SVM LDA Video Security 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of CSE & ITJIITNoidaIndia

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