Spam ∅

collect scrape comments from all also detection f1 popular youtube a metrics which spam these fed using score into bayes labeled naive spam. comments the linus developed criteria youtube youtube was was the browser were the these youtube naive that students from will the api. to as for detect using dataset data website set website developed allows gain bayes api model of model comment. following algorithm developed then evaluated learning given the and a the university taken bayes or ham was machine then bayes to integrated bicol the a accuracy, and was collected, youtube dataset by tool naive browser data cleaned, to and classifier determines was spam what videos user the satisfying was tips classifier manually platform. this scraped the training a that of precision, get using and score. data of tech from this classification. to spam the model. as metrics summarize, makes a developers using as recall, the api algorithm and extension such algorithm that channels a this to for or extension uses collect accurate from such a to be evaluation naive data to from made an pewdiepie.