@article{1546,
  author = {Rashid Chowdhury, Md. Nuruddin Monsur Adnan, G. A. N Mahmud, Rashedur M Rahman},
  title = {A Data Mining based Spam Detection System for YouTube},
  journal = {Journal of Information & Systems Management},
  year = {2014},
  volume = {4},
  number = {2},
  doi = {},
  url = {http://www.dline.info/jism/fulltext/v4n2/2.pdf},
  abstract = {We have entered the era of social media networks represented by Facebook, Twitter, YouTube and Flickr. Internet users now spend more time on social networks than search engines. Business entities or public figures set up social networking pages to enhance direct interactions with on- line users. Social media systems heavily depend on users for content contribution and sharing. Information is spread across social networks quickly and effectively. However, at the same time social media networks become susceptible to different types of unwanted and malicious spammer. In order to increase the popularity of a video, marketing advertisements or simply pollute the system malicious users may post video response spam. A video spam is a video response whose content is not related to the topic being discussed in that particular video. In this project we consider finding out relation among different attribute that could lead to video spammers. We first construct moderate test collection of YouTube users, and manually classify them as either legitimate users or spammers. We then devise a number of attributes of video users and their social behavior which could potentially be used to detect spammers. Employing these attributes, we apply MICROSOFTs SQL server data mining tools (SSDT) to provide a heuristic for classifying an arbitrary video as either legitimate or spam. We then show that our approach succeeds at detecting much of the spam while only falsely classifying a small percentage of the legitimate videos as spam. Our results highlight the most important attributes for video response spam detection.},
}