A system to filter unsolicited texts from social learning networks

In the present day scenario online social networks (OSN) are very trendy and one of the most interactive medium to share, communicate and exchange numerous types of information like text, image, audio, video etc. All these publicly shared information are explicitly viewed by connected people in the blog or networks and having an enormous social impact in human mind. Posting or commenting on particular public/private areas called wall, may include superfluous messages or sensitive data. Information filtering can therefore have a solid influence in online social networks and it can be used to give users the facility to organize the messages written on public areas by filtering out unwanted wordings. In this paper, we have proposed a system which may allow OSN users to have a direct control on posting or commenting on their walls with the help of information filtering. This is achieved through text pattern matching system, that allows users to filter their open space and a privilege to add new words treated as unwanted. For experimental analysis a test social learning website is designed and some unwanted words/texts are kept as blacklisted vocabulary. To provide control to the user, pattern matching of texts are done with the blacklisted vocabulary. If it passes then only text can be posted on someone´s wall, otherwise text will be blurred or encoded with special symbols. Analysis of experimental results shows high accuracy of the proposed system.