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@article{155003,
        author = {Prathmesh Paware and Sahil Gargate and Shrushti Pawar and Ashwini Kawade and Swati Patil},
        title = {YouTube Spam Filter Using Machine Leaning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {1048-1051},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155003},
        abstract = {The profit promoted by Google in its spick-and-span video distribution platform YouTube has attracted a growing scope of usercommunity. However, such success has attracted malevolent people who want to promote their videos or bear viruses and malware. Since YouTube offers restricted tools for comment moderation, the spam volume is shockingly increasing that's leading homeowners of known channels to disable the comments section in their videos. Automatic comment spam filtering on YouTube might be a challenge even for established classification ways since the messages unit terribly short and sometimes rife with slangs, symbols, and elisions. We've tested a number of high-performance classification algorithms for this purpose during this project. The math analysis of results indicates that with 99.9% of confidence level Bernoulli Naive Bayes, Decision trees, Logistic Regression, Random forests, Linear and Gaussian SVM’s area unit statistically equivalent. Therefore, it is vital to look out how to note these videos and report them before they're viewed by innocent user.},
        keywords = {Machine learning, Random Forests, Logistic Regression, Bernoulli Naïve Bayes, Decision trees, linear and Gaussian SVMs.},
        month = {},
        }