  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 update their opinions about a particular topic by learning from the
opinions shared by their friends. In this context, can we learn a data-driven
model of opinion dynamics that is able to accurately forecast opinions from
users? In this paper, we introduce SLANT, a probabilistic modeling framework of
opinion dynamics, which represents users opinions over time by means of marked
jump diffusion stochastic differential equations, and allows for efficient
model simulation and parameter estimation from historical fine grained event
data. We then leverage our framework to derive a set of efficient predictive
formulas for opinion forecasting and identify conditions under which opinions
converge to a steady state. Experiments on data gathered from Twitter show that
our model provides a good fit to the data and our formulas achieve more
accurate forecasting than alternatives.
