Informational Retrieval Using Crawler & Protecting Social Networking Data from Information leakage

Online social networks, such as Facebook, Twitter, Yahoo!, Google+ are utilized by many people. These networks allow users to publish details about themselves and to connect to their friends. Some of the information revealed inside these networks is meant to be private or public. Yet it is possible to use learning algorithms on released data to predict private or public information and use the classification algorithm on the collected data. In this paper, we explore how to get social networking data to predict information. We then devise possible classification techniques that could be used in various situations. Then, we explore the effectiveness of these techniques and attempt to use. We collect the different information from the users groups. On which we concluded the classification of that data. By using the various algorithms we can predict information of users. Crawler programs for current profile work. We constructed a spider that crawls & indexes FACBOOK. In this paper we focus on crawler programs that proved to be an effective tool of data base. In this paper we elaborate the use of data mining technique to help retailers to identify user profile for a retail store and improved. The aim is to judge the accuracy of different data mining algorithms on various data sets. The performance analysis depends on many factors encompassing test mode, different nature of data sets, and size of data set. Keyword: Social network analysis, data mining, social network data,WEKA.