AN AGENT-BASED SIMULATION STUDY OF THE DYNAMICS OF MOBILE VIRAL ADVERTISING

In the mobile era, mobile advertising is essential and has been developing very fast. Marketers are eager to turn to mobile viral advertising for benefiting from initially targeting customers. In this paper, we propose a computational model to reconstruct the spreading of advertisements in social networks. Using this computational model as a test-bed and running a series of virtual experiments, we acquire observations and implications about how to choose an initial set of people to maximize the performance of spreading advertisements. Also, we observe and analyze the impacts of the network structures including topology, size and density and the initial selected number of targeted people on the dynamics of mobile viral advertising. The virtual experiments also help us to examine the suitable policies for combining viral adverting with mass marketing in mobile commerce. In addition, we run virtual experiments (simulations) in a real mobile-online social network to validate the model and to provide an example for practitioners to apply this computational model. In practice, we use the attributes of people and the interest groups they are members of in social networks to infer the spreading probability between people.