Nothing But Net? Content and Network Factors in Information Diffusion
Information diffusion is the process in which nuggets of information spread in a network (typically a social network). This is a complex process that depends on the network topology, the social structures and the information itself (content/language). In this talk I will discuss information diffusion from these different yet complementary perspectives. In the first part of the talk I will focus on the features of the diffusing information. I will present a gradient boosted trees algorithm modified for learning user preferences of Twitter hashtags. In the second part of my talk I will focus on the network structure. I will use exponential random graph models (ERGM) in order to learn what latent factors contribute to network formation and I will show how network structure and social roles contribute to the information spread. Specifically, I will present promising results obtained on political networks in the American political systems and analysis of the partisan use of political hashtags.
In both parts I put emphasis on interpretable models that go beyond accurate prediction and facilitate a better understanding of complex social processes.