MACHINE LEARNING WITH DATA FRAME FOR CLASSIFICATION OF SPAM COMMENTS FROM YOUTUBE
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Abstract
YouTube, the world’s largest video sharing site, was founded in 2005 and acquired by Google in 2006. YouTube has grown tremendously as a video content platform, with the recent shift in online content to video. At present, more than 400 hours of video are uploaded, and 4.5 million videos are watched every minute on YouTube. It is easy for users to watch and upload videos without any restrictions. This great accessibility has increased the number of personal media, and some of them have become online influencers. YouTube creators can monetize if they have more than 1,000 subscribers and 4,000 hours of watch time for the last 12 months. Accordingly, spam comments are being created to promote their channels or videos in popular videos. Some creators closed the comment function due to aggression such as political comments, abusive speech, or derogatory comments not related to their videos. YouTube has its own spam filtering system, though there are still spam comments that are not being caught
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