Sociologically-Informed Graph Neural Network for Opinion Prediction

Abstract

Social media platforms has long served as open arenas where individuals discuss and change their opinions on various events, subsequently influencing the progression of these events. Public opinion, recognized as an important social signal, is instrumental in understanding the developmental patterns of social events and in guiding more informed responses. In light of this, we propose a sociologically-informed opinion prediction model, which integrates rich social interaction data with time series forecasting techniques using a graph neural network framework. This model, enriched by a sociological theoretical model, reflects the real-world dynamics of opinion evolution. Experimental results derived from three synthetic datasets and two real-world datasets indicate that incorporating user interaction data, along with more effective utilization of historical information, has led to a large improvement in the accuracy of opinion predictions. The source code and sample data for our study are available at https://github.com/RiikkaYang/SIGNN.