Learning Opinion Dynamics in Social Networks

Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often form their opinion about a particular topic by learning information about it from her peers. In this context, whenever a user posts a message about a topic, we observe a noisy estimate of her current opinion about it, however, the influence the user may have on other users’ opinions is hidden. In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which allows the underlying opinion of a user to be modulated by those expressed by her neighbors over time. We then identify a set of conditions under which users’ opinions converge to a steady state, find a linear relation between the initial and steady state opinions, and develop an efficient estimation method to fit the model parameters from historical fine-grained opinion and information diffusion event data. Experiments on data gathered from Twitter, Reddit and Amazon show that our model provides a good fit to the data and more accurate nowcasting and forecasting than alternatives.