The Survey Paper on Filter Unwanted Messages from Walls and Blocking Non-legitimate Users in OSN
Pallavi Shinde, Trupti Gedam · 2014
In recent years, Online Social Networks (OSNs) have become an vital part of daily life. Users build specific networks to represent their social relationships. Users can upload and share information associated to their personal lives. The privacy risks of such behavior are often ignored. And the basic issue in today On-line Social Networks is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Today OSNs provide very little or no support to prevent unwanted messages on user walls. For that purpose, we proposed a new system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that permits users to customize the filtering criteria to be applied to their walls, and a Machine Learning (ML) based soft classifier automatically labeling messages in support of content-based filtering. The system utilizes a ML soft classifier to enforce customizable content-dependent Filtering Rules. And the flexibility of the system in terms of filtering options is enhanced through the management of Blacklists. The proposed system gives security to the On-line Social Networks. Communication technology and information plays a vital role in today's networked society. It has affected the online interaction between users, who are aware of security applications and their implications on personal privacy. There is a requirement to develop a lot of security mechanisms for various communication technologies, significantly online social networks. OSNs provide very little or no support to prevent unwanted messages on user walls. With the lack of classification or filtering tools, the user receives all messages posted by the users he follows. In most cases, the user receive a noisy stream of updates. In OSNs, information filtering can even be used for a various, more distinct, purpose. This is due to the fact that in OSNs there is the possibility of posting or commenting other posts on particular public/private areas, known as general walls. In the proposed system Information filtering can therefore be used to give users the ability to automatically control the messages written on their own walls, by filtering out unwanted messages. The aim of the present work is therefore to propose and experimentally evaluate an automated system, called Filtered Wall (FW), able to filter unwanted messages from OSN user walls. We tend to exploit Machine Learning (ML) text categorization techniques (2) to automatically assign with every short text message a set of categories based on its content. The major efforts in building a robust short text classifier are focused in the extraction and selection of a group of characterizing and discriminate features. 2. Literature Review