Modeling User Arguments, Interactions, and Attributes for Stance Prediction in Online Debate Forums

Online debate forums are important social media for people to voice their opinions and debate with each other. Mining user stances or viewpoints from these forums has been a popular research topic. However, most current work does not address an important problem: for a specific issue, there may not be many users participating and expressing their opinions. Despite the sparsity of user stances, users may provide rich side information; for example, users may write arguments to back up their stances, interact with each other, and provide biographical information. In this work, we propose an integrated model to leverage side information. Our proposed method is a regression-based latent factor model which jointly models user arguments, interactions, and attributes. Our method can perform stance prediction for both warm-start and cold-start users. We demonstrate in experiments that our method has promising results on both micro-level and macro-level stance prediction.