Understanding and Predicting Question Subjectivity in Social Question and Answering

Abstract

The explosive popularity of social networking sites has provided an additional venue for online information seeking. By posting questions in their status updates, more and more people are turning to social networks to fulfill their information needs. Given that understanding individuals' information needs could improve the performance of question answering, in this paper, we model the task of intent detection as a binary classification problem, and thus for each question, two classes are defined: subjective and objective. We use a comprehensive set of lexical, syntactical, and contextual features to build the classifier and the experimental results show satisfactory classification performance. By applying the classifier on a larger dataset, we then present in-depth analyses to compare subjective and objective questions, in terms of the way they are being asked and answered. We find that the two types of questions exhibited very different characteristics, and further validate the expected benefits of differentiating questions according to their subjectivity orientations.