Paper Title
Spam Proof Tagging System Using Trust Modeling Algorithm

Tagging in online social networking site is very popular these days, as it facilitates search and retrieval of various resources such as text, images, videos, etc. Despite the advances in social networking over the past few decades, one of the important challenges that user continuously facing is spam. Noisy and spam annotations often make it difficult to perform an efficient search. The shared content is sometimes assigned with inappropriate tags for several reasons. Users may make mistakes while tagging and irrelevant tags and content may be maliciously added for their advertisement or selfpromotion. Consequently, assigning tags to resources has a risk that wrong or irrelevant tags eventually prevent users from the benefits of annotated content. One important challenge in tagging is to identify the legitimate tags for given content, and at the same time, to eliminate spam tags. Trust can predict the future behavior of users to avoid undesirable influences of untrust-worthy users. Here we proposed a trust-worthy system that has been designed with the objective to minimize spam tagging and posting in social networking sites with the adaptation of classification algorithms. Keywords— Tagging, Tagging system, Trust modeling, Tag spam.