Learning and Forecasting Opinion Dynamics in Social Networks

TL;DR

This paper introduces SLANT, a probabilistic framework using stochastic differential equations to model and forecast opinion dynamics on social media, demonstrating improved accuracy over existing methods.

Contribution

The paper presents a novel probabilistic model for opinion dynamics based on jump diffusion processes, enabling efficient forecasting and analysis of opinion convergence.

Findings

SLANT accurately fits Twitter opinion data

Forecasting formulas outperform alternatives

Opinions tend to converge under certain conditions

Abstract

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.