Social networks user modeling

Abstract

Social Networks (SN) is a source of great interest to researchers in the fields of scientific research, because of spread a very large number of SN and  the attention of quite a lot of peoples, as well as to the content of SNS of a large quantity and variety of transmitted data between one site and various sites (such as photos, messages, and personal information, news, websites, scientific research, and other information). Within this study we study the attributes and activities of users of social networking sites (Facebook) and identify the most important and effective elements in activating the site, where accumulate information on social sites users generally speaking and information on facebook in particular, the methodology incorporates a two-stage cross mechanism. neural network for clustering data and rough sets theory (Johnson Reducer and naïve Bayes Classifier) for classification and  analysis. The simulation model which we carried out, demonstrated really good brings about the diagnosing process that contacted (0. 94)% of the accuracy diagnosis..