International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 2, February 2014) 752 Unwanted Message Filtering System from OSNs User’s Wall Using Customizable Filtering Rules and Black list Techniques. Amruta Kachole 1 , S. D. Jondhale 2 1 Student (ME Computer), 2 Professor, SVIT College, Chincholi, Nasik Abstract - Users have ability to keep in touch with his/her friends by exchanging different types of information or messages like text, audio and video data. Today’s OSNs (Online Social Network System) do not provide much support to the users to avoid unwanted messages displayed on their own private space called in general wall. So, in this paper we present OSNs system which gives ability to users to control the messages posted on their own private space to avoid unwanted messages displayed. Customizable Filtering Rules are used to filter the unwanted messages from OSNs users wall as well as Machine learning approach, Short Text Classification and Black list techniques are applied on Users Wall. Index Terms - On-line Social Network, Information filtering, short text classification. I. INTRODUCTION Today’s modern life is totally based on Internet. Now a days people cannot imagine life without Internet. Also, OSNs are just a part of modern life. From last few years people share their views, ideas, information with each other using social networking sites. Such communications may involve different types of contents like text, image, audio and video data. But, in today’s OSN , there is a very high chance of posting unwanted content on particular public/private areas, called in general walls. So, to control this type of activity and prevent the unwanted messages which are written on user’s wall we can implement filtering rules (FR) in our system. Also, Black List (BL) will maintain in this system .We present this system as www.winow.in on the internet. It can be used to give users the ability to automatically control the messages written on their own walls, by filtering out unwanted messages. The huge and dynamic character of these data creates the premise for the employment of web content mining strategies aimed to automatically discover useful information dormant within the data. OSNs provide support to prevent unwanted messages on user walls. For example, Facebook allows users to state who is allowed to insert messages in their walls (i.e., friends, friends of friends, or defined groups of friends). However, no content-based preferences are supported and therefore it is not possible to prevent undesired messages, such as political or vulgar ones, no matter of the user who posts them. Providing this service is not only a matter of using previously defined web content mining techniques for a different application, rather it requires to design ad hoc classification strategies. This is because wall messages are constituted by short text for which traditional classification methods have serious limitations since short texts do not provide sufficient word occurrences. II. RELATED WORK In www.winow.in information filtering techniques are used to remove unwanted contents by using customizable content based filtering rules, Machine learning approach; according to user’s interest and recommends an item. Recommender systems works in following ways  Content based filtering  Collaborative filtering  Policy based filtering A. Content-based filtering In content based filtering to check the user’s interest and previous activity as well as item uses by users best match is found [10]. For example OSNs such as Facebook, Orkut used content based filtering policy. In that by checking users profile attributes like education, work area, hobbies etc. suggested friend request may send. The main purpose of content based filtering, the system is able to learn from user’s actions related to a particular content source and use them for other content types. B. Collaborative filtering In collaborative filtering information will be selected on the basis of user’s preferences, actions, predicts, likes, and dislikes. Match all this information with other users to find out similar items. Large dataset is required for collaborative filtering system. According to user’s likes and dislikes items are rated. C. Policy-based filtering In policy based filtering system users filtering ability is represented to filter wall messages according to filtering criteria of the user. Twitter is the best example for policy based filtering.[1] In that communication policy can be defines between two communicating parties.