Despite the popularity of mobile pay-for-answer Q&A services, little is known about the people who answer questions on these services. In this paper we examine 18.8 million question and answer pairs from Jisiklog, the largest mobile payforanswer Q&A service in Korea, and the results of a complementary survey study of 245 Jisiklog workers. The data are used to investigate key motivators of participation, working strategies of experienced users, and longitudinal interaction dynamics. We find that answerers are rarely motivated by social factors but are motivated by financial incentives and intrinsic motives. Additionally, although answers are provided quickly, an answerer's topic selection tends to be broad, with experienced workers employing unique strategies to answer questions and judge relevance. Finally, analysis of longitudinal working patterns and community dynamics demonstrate the robustness of mobile pay-for-answer Q&A. These findings have significant implications on the design of mobile pay-for-answer Q&A.

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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.