Behzad Rezaie - Department of Computer Engineering and IT, Shahrood University of Technology
Morteza Zahedi - Department of Computer Engineering and IT, Shahrood University of Technology
Hoda Mashayekhi - Department of Computer Engineering and IT, Shahrood University of Technology

Social networks like Twitter and Facebook are overwhelming our daily life nowadays. One of the most important topics studied in the last few years in the field of social network analysis, is identification of the most influential users. Presidential candidates, large and small businesses, and other groups and individuals in social networks try to identify the influential users so that such users support them or their products by advertisement. A number of tools like Klout1, TwitterCounter2, PeerIndex3, and Twitaholic4 are developed with the aim of analyzing and ranking millions of users in Twitter. While most of the tools do not reveal their algorithms and methods for calculation of users’ influence, it is important for users to know how to improve their rank in such services. Association rule mining is employed in this study to detect how Twitter users can improve their influence in Klout, as the most popular Twitter ranking tool.The results show that the number of followers and the number of list members are among the most important criteria for influence in Klout

influential users, social network analysis, association rule mining