Collective Stance Classification of Posts in Online Debate Forums

Online debate sites are a large source of informal and opinion-sharing dialogue on current socio-political issues. Inferring users ’ stance (PRO or CON) towards dis-cussion topics in domains such as politics or news is an important problem, and is of utility to researchers, government or-ganizations, and companies. Predicting users ’ stance supports identification of so-cial and political groups, building of better recommender systems, and personaliza-tion of users ’ information preferences to their ideological beliefs. In this paper, we develop a novel collective classification approach to stance classification, which makes use of both structural and linguis-tic features, and which collectively labels the posts ’ stance across a network of the users ’ posts. We identify both linguistic features of the posts and features that cap-ture the underlying relationships between posts and users. We use probabilistic soft logic (PSL) (Bach et al., 2013) to model post stance by leveraging both these local linguistic features as well as the observed network structure of the posts to reason over the dataset. We evaluate our approach on 4FORUMS (Walker et al., 2012b), a col-lection of discussions from an online de-bate site on issues ranging from gun con-trol to gay marriage. We show that our col-lective classification model is able to eas-ily incorporate rich, relational information and outperforms a local model which uses only linguistic information.