• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.21-35
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• 2010

The popularity of question answering communities such as Yahoo! Answers and Naver Knowledge-iN and increasing doubts about the competence of lay information providers prompted this study to explore answerers' strategies to provide a credible answer in a question answering community. Forty-four active answerers in Yahoo! Answers were included in this study, and interviews were conducted through email, chat, and over the telephone. This study identified a set of information sources the answerers used, an array of important strategies to provide a credible answer, and their perception of self-claimed expertise. Implications of results were discussed in the context of user instruction.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.135-154
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• 2012

Social Q&A sites such as Yahoo! Answers and Naver Knowledge-iN have become a viable method for information seeking and sharing on the Web. Considering their immense popularity and growing concerns about their validity as information sources, questions about the credibility of the information provided on social Q&As are timely. Therefore, this paper summarizes recent research on credibility related to the social Q&A context, identifies research gaps, and presents a research agenda for future research to advance this newly developing area.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.43-53
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• 2009

Knowledge retrieval service that uses collective intelligence which has special quality of open structure and can share the accumulative data is gaining popularity. However, acquiring the right needs for users from massive public knowledge is getting harder. Recently, search results from Google which is known for it's exquisite algorism, shows results for collective intelligence such as Wikipedia, Yahoo Q/A at the highest rank. Objective of this paper is to show that most answers come from human and to find the most influential people in on-line knowledge retrieval service. Hereupon, this paper suggest the influence value calculation algorism by analyzing user relation as centrality which social network is based on user activeness and reliance in Naver 지식iN. The influence value calculated by the suggested algorism will be an important index in distinguishing reliable and the right user for the question by ranking users with troubleshooting solutions in the knowledge retrieval service. This will contribute in search satisfaction by acquiring the right information and knowledge for the users which is the most important objective for knowledge retrieval service.