Opinion Formation Forecasts in Social Networks: A Graph Convolutional Neural Network Approach

Opinion formation in social networks has a significant impact on the society. Consequently, accurate opinion formation forecasts that can be effectively controlled and handled are particularly desirable. Current opinion prediction is primarily based on two methods: a. modeling and simulation and b. machine learning. Although modeling and simulation approaches have the advantage of incorporating a priori knowledge, such as topological information about social networks, existing studies suggest that the predictive accuracy of such models may be lower than expected. Neural network-based methods demonstrate improved performance; however, incorporating a priori knowledge in these models is difficult. To address these limitations, in this study, we develop a graph convolutional neural network-based opinion evolution prediction method, called GCNN. The method follows the opinion dynamics in the model training process to directly involve the topology of the network in opinion prediction. This mechanism shrinks the search space of the neural network parameters and improves the accuracy rate by exploiting the network topology as a priori knowledge. To validate our method, we perform experimental validation on nine synthetic datasets and one real dataset. The results reveal that our method outperforms benchmark models. In addition, our method exhibits superior accuracy rates compared to other candidate models, even when trained on only one-sixth of the training data typically used by other models.