Recently questioning and answering (Q&A) communities that facilitate knowledge sharing among people have been introduced to the mobile environments such as Naver Mobile Q&A and ChaCha. These mobile Q&A services are very different from traditional Q&A sites in that questions/answers are short in length and are exchanged via mobile devices (e.g., SMS or mobile Internet). While traditional Q&A sites have been well investigated, so far little is known about the mobile Q&A usage. To understand mobile Q&A usage, we analyzed 2.4 million question/answer pairs spanning a 14 month period from Naver Mobile Q&A and performed a complementary survey study of 555 active mobile Q&A users. We find that mobile Q&A is deeply wired into users' everyday life activities-its usage is largely dependent on users' spatial, temporal, and social contexts; the key factors of mobile Q&A usage are accessibility/convenience of mobile Q&A, promptness of receiving answers, and users' satisficing behavior of information seeking (i.e., minimizing efforts and settling with good enough information). We also observe that users tend to seek more factual information attributed to everyday life activities than they do on traditional Q&A sites and that they exhibit unique interaction patterns such as repeating and refining questions as coping strategies in seeking information needs. Our main findings reported in the paper have significant implications on the design of mobile Q&A systems.

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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