Question popularity analysis and prediction in community question answering services.

Abstract

With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of efforts have been taken to explore the questions and answers retrieval in CQA services so as to help users to finding the similar questions or the right answers. However, to our knowledge, less attention has been paid so far to question popularity in CQA. Question popularity can reflect the attention and interest of users. Hence, predicting question popularity can better capture the users' interest so as to improve the users' experience. Meanwhile, it can also promote the development of the community. In this paper, we investigate the problem of predicting question popularity in CQA. We first explore the factors that have impact on question popularity by employing statistical analysis. We then propose a supervised machine learning approach to model these factors for question popularity prediction. The experimental results show that our proposed approach can effectively distinguish the popular questions from unpopular ones in the Yahoo! Answers question and answer repository.