Evaluating user influence based on Web2.0 UGC

With the development of Social Networking Services, users can publish and receive information expediently. Social marketing emerged at this circumstance with an important need to study the influence of marketing activities in network. Based on the research of user influence, choosing the most influential users can improve marketing effectiveness greatly. With the appearance of Facebook, Twitter, and other social networks, more and more scholars began to study user influence in such complex and huge network. In this paper, we make a quantitative analysis to evaluate user influence and classify users into different categories. We synthetically consider various user attributes, including the static social relationships and dynamic social activities. We add new components to improve PageRank algorithm and propose a behavior-based ranking(BBR) algorithm. Experiments show that the improved BBR algorithm has a satisfied performance.