Overview / Usage

Ever since its development in 2005, YouTube has been providing a vital social media platform for video sharing. Unfortunately, YouTube users may have malicious intentions, such as disseminating malware and profanity. One way to do so is using the comment field for this purpose. Although YouTube provides a built-in tool for spam control, yet it is insufficient for combating malicious and spam contents within the comments. In this work, we\ have evaluated several top-performance classification techniques\ for such purpose. The statistical analysis of results indicates that, with 99.9% of Naive Bayes, KNN, SVMs are statistically equivalent. Based on this, we have also offered the Tube Spam an accurate online system to filter comments posted on YouTube.

Methodology / Approach

The “Classification” field is presenting the essence of the comment “spam” or “ham”.The first stage in data processing is isolating comments column from the dataset as one vector to be processed solely. The comments were regarded as bags of words and processed on this basis. No further preprocessing was done to keep the originality of the text. Essentially, each word was considered as a set of terms, and each term is composing a word of two or more alphabetical, underscore or a number.we will make use of Naïve Bayes, K-nearest neighbor and KNN algorithm for the purpose of spam