Expert finding on social network with link analysis approach

With the appearance of social networks in the Internet, the communications between people took a new form. Nowadays, lots of people with different goals are registered in social networks and do wide range of activities. One of the most important feature of social networks is knowledge sharing. The main problem regarding to this issue is a wide range of shared knowledge and there is no mechanism to determine their validity. So, the knowledge shared on social networks could not be trusted. By finding experts in social networks and determining their level of knowledge, the validity of their posts could be determined. Therefore a solution to the mentioned problem is to provide a method for expert finding. In this research a novel model based on social network analysis is proposed to find the experts who are the members of social networks by means of business intelligence approach. This model is verified by real data from Friendfeed social network. First, data is extracted, transformed and loaded to data warehouse with ETL processes. Then a new ranking algorithm is proposed for finding the experts, and finally the obtained results are compared to the experts´ opinions utilizing spearman´s correlation function.