Timezone: »

Learning and Forecasting Opinion Dynamics in Social Networks
Abir De · Isabel Valera · Niloy Ganguly · Sourangshu Bhattacharya · Manuel Gomez Rodriguez

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #163

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 users' opinions? 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.

Author Information

Abir De (IIT Kharagpur)
Isabel Valera (Max Planck Institute for Software Systems (MPI-SWS))
Niloy Ganguly (IIT Kharagpur)
Sourangshu Bhattacharya (IIT Kharagpur)
Manuel Gomez Rodriguez (MPI-SWS)

More from the Same Authors