Support or oppose? Classifying positions in online debates from reply activities and opinion expressions

We propose a method for the task of identifying the general positions of users in online debates, i.e., support or oppose the main topic of an online debate, by exploiting local information in their remarks within the debate. An online debate is a forum where each user post an opinion on a particular topic while other users state their positions by posting their remarks within the debate. The supporting or opposing remarks are made by directly replying to the opinion, or indirectly to other remarks (to express local agreement or disagreement), which makes the task of identifying users'general positions difficult. A prior study has shown that a linkbased method, which completely ignores the content of the remarks, can achieve higher accuracy for the identification task than methods based solely on the contents of the remarks. In this paper, we show that utilizing the textual content of the remarks into the link-based method can yield higher accuracy in the identification task.