Measuring User Influence, Susceptibility and Cynicalness in Sentiment Diffusion
Abstract
Diffusion in social networks is an important research topic lately due to massive amount of information shared on social media and Web. As information diffuses, users express sentiments which can affect the sentiments of others. In this paper, we analyze how users reinforce or modify sentiment of one another based on a set of inter-dependent latent user factors as they are engaged in diffusion of event information. We introduce these sentiment-based latent user factors, namely influence, susceptibility and cynicalness. We also propose the ISC model to relate the three factors together and develop an iterative computation approach to derive them simultaneously. We evaluate the ISC model by conducting experiments on two separate sets of Twitter data collected from two real world events. The experiments show the top influential users tend to stay consistently influential while susceptibility and cynicalness of users could changed significantly across events.