Content-Based Classification Approach for Video-Spam Identification

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.