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Spammer detection and tagging based user generated video search system — A survey

In this modern world everything is done using online. Most people tend to watch films, sports, songs and also play games in online. One such mostly used site to do all this activity is the You Tube. In such online sites there is a possibility to introduce unwanted information which leads to wastage of user time in searching the content. These unwanted information present in the online sites is referred to us the spammers. In the existing work related to the you Tube the user have to search for the videos which takes a long time and irrelevant videos not related to the user search is displayed. The aim of the proposed work is that, User-generated content (UGC) video systems by definition heavily depend on the input of their community of users and their social interactions for video diffusion and opinion sharing. As a UGC system can achieve a larger audience through improved connectivity, findings motivate to propose a mean to enhance the users' connectivity by taking benefit of friend recommendation and spammer detection of the online videos. The algorithm active lazy fuzzy classifier algorithm used to detect spammers. Two similarity metrics are constructed based on users' interests that are derived from their uploads and favorite tagging of videos. Two friend recommendation algorithms are then proposed. The algorithms use public information provided by users to suggest potential friends with similar interests as measured by the similarity metrics. This paper presents the survey of spammer detection and their technologies and also recommending videos to the user by means of friend function which reduces the time of searching the videos and various methods for rating the videos.