Booming Up the Long Tails: Discovering Potentially Contributive Users in Community-Based Question Answering Services

Abstract

Community-based question answering (CQA) services such as Yahoo! Answers have been widely used by Internet users to get the answers for their inquiries. The CQA services totally rely on the contributions by the users. However, it is known that newcomers are prone to lose their interests and leave the communities. Thus, finding expert users in an early phase when they are still active is essential to improve the chances of motivating them to contribute to the communities further. In this paper, we propose a novel approach to discovering "potentially" contributive users from recently-joined users in CQA services. The likelihood of becoming a contributive user is defined by the user's expertise as well as availability, which we call the answer affordance. The main technical difficulty lies in the fact that such recently-joined users do not have abundant information accumulated for many years. We utilize a user's productive vocabulary to mitigate the lack of available information since the vocabulary is the most fundamental element that reveals his/her knowledge. Extensive experiments were conducted with a huge data set of Naver Knowledge-In (KiN), which is the dominating CQA service in Korea. We demonstrate that the top rankers selected by the answer affordance outperformed those by KiN in terms of the amount of answering activity.