Discovering Influential Nodes for Viral Marketing

High cost and uncertainty are problems of marketing. Influential online product reviews are more powerful than firm´s advertisements. The key of viral marketing is to discover the viruses for efficiently spreading product impressions. In this paper, a model combined with mining techniques and adaptive RFM is proposed to evaluate the influential power of online reviewers. The modified PMI equation quantifies the review value and the RFM concept is used to consider the writing status of reviewers for the influence calculation. The artificial neural network is also adopted to train the appropriate network structure in our model. Trust, the most common influential power indicator, is then used to evaluate our model. The results showed that our model outperforms two general methods in selecting influential reviewers. Our work can accurately point out which reviewer to be selected to become the virus.