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Graduate Projects


Project ID: 436
Author: Srilaxmi Gandra
Project Title: Implementation Of Prototype To Detect Spam In YouTube Using The Application TubeKit And Naïve Bayes Algorithm
Semester: 1 2014
Committe Chair: Dr. Mario Garcia
Committee Member 1: Dr. David Thomas
Project Description: A lot of time of the internet user is invested on social network rather than search engines. Many of the business organizations and celebrities are taking advantage of this service by setting up social networking pages to improve direct communication with online users. Social media heavily relies on user-generated content which makes them incredibly powerful. In a very quick and effective way the information is passed across YouTube. However, YouTube network is prone to various types of unnecessary and malicious spammer activities. To maximize the popularity of a video, spammers post comments and video links, where the comment has irrelevant content of the subject being discussed. If spams are left unchecked, it attenuates resources sharing, inter communication and openness. Hence there is a crucial need for security solution and a technique to combat video spamming in YouTube. In this proposed system, the Naive Baye’s algorithm which is based on Baye’s Theorem is used. In this system, using Tube kit, data of YouTube videos are collected and manually categorizes it as legitimate or a spam video. Various attributes that could lead to spammers are explored using TubeKit. Later Microsoft SQL Server Data Mining Tools is used to determine if a video is legitimate or spam.
Project URL:   436.pdf