An action–reaction influence model relying on OSN user-generated content

Due to the sustained popularization of Online Social Networks (OSNs), it has become of interest for a variety of domains of applications to correctly characterize how the behavior of an individual user can be influenced by the actions of other users in a network. Additionally, the richness of available features in modern OSNs highlights the growing importance of user-generated data in establishing user relations. In this paper, we follow a data-driven methodology and propose a diffusion algorithm designed around user-to-content relationships and an action–reaction paradigm. Crucially, we design our approach by integrating different cross-disciplinary theories of how users influence each other. Thus, we enrich the influence maximization task with a psychological dimension and define a model that ties influence diffusion to recurrent users’ behavior from OSN logs, considering relationships between users mediated by user-generated content. We evaluate our approach over the Yahoo Flickr Creative Commons 100 Million real-world dataset. We measure efficiency and effectiveness by analyzing scalability and spread efficacy and show how our model outperforms existing state-of-the-art methods.