Content-Based Network Influence Probabilities: Extraction and Application

The diffusion of information and influence in networks shapes ideas, habits, and behaviors. Several diffusion control methods have been proposed to harness, maximize, limit, or direct such diffusion processes in desirable ways. State-of-the-art algorithms for prescriptive fine-grained diffusion control rely on simple models, most prominently the Independent Cascade (IC) model, rather than on advanced machine learning approaches. The simplicity of such models can be an advantage. Yet, to exploit this advantage, one needs not only well-designed algorithms, but also a powerful model-training framework that yields well-informed models. Unfortunately, much research effort has been devoted to algorithm design, while the development of techniques for informing the underlying model has been largely neglected. We propose a new content analysis workflow that derives realistic IC model parameters for diffusion control algorithms. We rely on a log of user text messages to derive a measure of similarity among those messages, and therefrom calculate the probability that one node influences another. We evaluate our model in terms of its predictive power and apply it to two representative diffusion control problems under the IC model. Our results showcase the capacity of our methods to make correct predictions, and provide the first, to our knowledge, study of diffusion control problems with a real-world probability model.