Differential adaptive diffusion: Understanding diversity and learning whom to trust in viral marketing

Viral marketing mechanisms use the existing social network between customers to spread information about products and encourage product adoption. Existing viral marketing models focus on the dynamics of the diffusion process, however they typically: (a) only consider a single product campaign and (b) fail to model the evolution of the social network, as the trust between individuals changes over time, during the course of multiple campaigns. In this work, we propose an adaptive vi- ral marketing model which captures: (1) multiple different product campaigns, (2) the diversity in customer preferences among different product categories, and (3) changing con- fidence in peers’ recommendations over time. By applying our model to a real-world network extracted from the Digg social news website, we provide insights into the effects of network dynamics on the different products’ adoption. Our experiments show that our proposed model outperforms ear- lier non-adaptive diffusion models in predicting future prod- uct adoptions. We also show how this model can be used to explore new viral marketing strategies that are more success- ful than classic strategies which ignore the dynamic nature of social networks.