Neural opinion dynamics model for the prediction of user-level stance dynamics

•A novel neural dynamic model for the prediction of user-level stance dynamics.•Neighbors’ topic-associated impacts are modelled by an attention mechanism.•User’s posting behaviors are simulated by a Recurrent Neural Network.•The model’s parameters are continuously updated with the stream data.•Experiments against static and dynamic alternatives show the effectiveness. Social media platforms allow users to express their opinions towards various topics online. Oftentimes, users’ opinions are not static, but might be changed over time due to the influences from their neighbors in social networks or updated based on arguments encountered that undermine their beliefs. In this paper, we propose to use a Recurrent Neural Network (RNN) to model each user’s posting behaviors on Twitter and incorporate their neighbors’ topic-associated context as attention signals using an attention mechanism for user-level stance prediction. Moreover, our proposed model operates in an online setting in that its parameters are continuously updated with the Twitter stream data and can be used to predict user’s topic-dependent stance. Detailed evaluation on two Twitter datasets, related to Brexit and US General Election, justifies the superior performance of our neural opinion dynamics model over both static and dynamic alternatives for user-level stance prediction.