Analyzing and Predicting Not-Answered Questions in Community-Based Question Answering Services

Abstract

This paper focuses on analyzing and predicting not-answered questions in Community based Question Answering (CQA) services, such as Yahoo! Answers. In CQA, users express their information needs by submitting questions and await answers from other users. One of the key problems of this pattern is that sometimes no one helps to give answers. In this paper, we analyze the not-answered questions and give a first try of predicting whether questions will receive answers. More specifically, we first analyze the questions of Yahoo! Answers based on the features selected from different perspectives. Then, we formalize the prediction problem as supervised learning task and leverage the proposed features to make predictions. Extensive experiments are made on 76,251 questions collected from Yahoo! Answers.