By Lina Sorg
In today’s technologically-advanced society, social networking dominates how people exchange information and interact with each other. Active individuals on sites like Facebook and Twitter share breaking or controversial news almost immediately, initiating near-instantaneous discussion and debate among users. As a result, many users’ personal ideas are influenced by those of their vocal peers, so much so that political campaigns often monitor trending opinions on social networking sites.
The goal in developing SLANT was to design a realistic model that fits real fine-grained opinion traces, predicts opinions overtime, and reveals how opinions influence one another. “There are a lot of theoretical models of opinion dynamics, but not many that distinguish between latent and expressed opinions,” Rodriguez said. “And opinions are not updated sequentially in discrete time.” As a consequence, existing models do not learn from fine-grained data and thus often yield inaccurate predictions. Rodriguez’s framework models each user’s messages as a marked point process to keep track of opinions. In this way, the model follows stubborn users, compromised users, and conforming users, enabling a theoretical analysis of both the transient and steady state.
Rodriguez then created a method of estimation based on historical diffusion of data that scales to networks with millions of nodes and events. He evaluated SLANT with network data from real-world events, such as the upcoming United States presidential elections, and tested his model on sites like Twitter, Amazon, and Reddit. For each dataset, Rodriguez built a collection of events to fit and evaluate the model. Although forecasted opinions become less accurate as the time increases, results indicated that SLANT fits online communication data accurately and may provide more precise predictions than prior methods. Rodriguez hopes to explore questions of nonlinear dependence and the development of opinion control methods as he continues work on his model.