Investigating Factors Affecting Automated Question Triage for Social Reference: A Study of Adopting Decision Factors from Digital Reference
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The efficiency and quality of the social reference sites are being challenged because a large quantity of the questions have not been answered or satisfied for quite a long time. Main goal of this study is to investigate important factors that affect the performance of question triage to relevant answerers in the context of social reference. To achieve the goal, expert finding techniques were used to construct an automated question triage approach to resolve this problem. Furthermore, important factors affecting triage decisions in digital reference were first examined, and extended them to the social reference setting by investigating important factors affecting the performance of automated question triage in the social reference setting. The study was conducted using question-answer pairs collected from Ask Metafilter. For the evaluation, logistic regression analyses were conducted to examine which factors would significantly affect the performance of predicting relevant answerers to questions. The results of the current study have important implications for research and practice in automated question triage for social reference. Furthermore, the results will offer insights into designing user-participatory digital reference systems.
Abstract
The efficiency and quality of the social reference sites are being challenged because a large quantity of the questions have not been answered or satisfied for quite a long time. Main goal of this study is to investigate important factors that affect the performance of question triage to relevant answerers in the context of social reference. To achieve the goal, expert finding techniques were used to construct an automated question triage approach to resolve this problem. Furthermore, important factors affecting triage decisions in digital reference were first examined, and extended them to the social reference setting by investigating important factors affecting the performance of automated question triage in the social reference setting. The study was conducted using question-answer pairs collected from Ask Metafilter. For the evaluation, logistic regression analyses were conducted to examine which factors would significantly affect the performance of predicting relevant answerers to questions. The results of the current study have important implications for research and practice in automated question triage for social reference. Furthermore, the results will offer insights into designing user-participatory digital reference systems.
참고문헌 (24)
- Agichtein, E., "Finding High-Quality Content in Social Media", Proceedings of the 2008 International Conference on Web Search and Data Mining (2008)
- Balog, K., "Formal models for expert finding in enterprise corpora", Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (2006)
- Bian, J., "Finding the Right Facts in the Crowd: Factoid Question Answering over Social Media", Proceedings of the 17th international conference on World Wide Web (2008)
- Blei, D. M., "Latent dirichlet allocation", The Journal of Machine Learning Research, 3, pp.993~1022 (2003)
- Bouguessa, M., "Identifying authoritative actors in question-answering forums: the case of Yahoo! answers", Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (2008)
- Gazan, R., "Seekers, sloths and social reference : Homework questions submitted to a question-answering community", New Review of Hypermedia and Multimedia, 13(2), pp.239~248 (2007)
- Goldman, A. I., "Knowledge in a social world" (1999), Oxford University Press
- Harper, F. M., "Facts or friends?: distinguishing informational and conversational questions in social Q&A sites", Proceedings of the 27th international conference on Human factors in computing systems (2009)
- Jeon, J., "Finding similar questions in large question and answer archives", Proceedings of the 14th ACM international conference on Information and knowledge management (2005)
- Jurczyk, P., "Discovering authorities in question answer communities by using link analysis", Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007), ACM
- Kim, S., "Best-answer selection criteria in a social Q&A site from the user-oriented relevance perspective", Proceedings of the American Society for Information Science and Technology, 44(1), pp.1~15 (2007)
- Lee, C. T., "Model for voter scoring and best answer selection in community Q&A services", Proceedings of the Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09. IEEE, ACM International Joint Conferences, pp.2009[DOI]
- Li, B., "Routing questions to appropriate answerers in community question answering services", Proceedings of the 19th ACM international conference on Information and knowledge management (2010)
- Liu, M., "Predicting best answerers for new questions in community question answering", Web-Age Information Management, pp.127~138 (2010)
- Nam, K. K., "Questions in, knowledge in?: a study of naver's question answering community", Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2009)
- Park, J. D., "Automated Question Triage for Social Reference: A Study on Adopting Decision Factors from Digital Reference", PhD, pp.2013
- Pomerantz, J., "Factors Influencing Digital Reference Triage : A Think-Aloud Study", Library Quarterly, 74(3), pp.235~264 (2004)
- Pomerantz, J., "Digital Reference Triage : Factors Influencing Question Routing and Assignment", Library Quarterly, 73(2), pp.103~ (2003)
- Qu, M., "Probabilistic question recommendation for question answering communities", Proceedings of the 18th international conference on World wide web (2009)
- Shachaf, P., "Social reference : Toward a unifying theory", Library & Information Science Research, 32(1), pp.66~76 (2010)
- Shah, C., "Exploring characteristics and effects of user participation in online social Q&A sites", First Monday, 13(9) (2008)
- Shah, C., "Evaluating and predicting answer quality in community QA", Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (2010)
- Steyvers, M., "Probabilistic author-topic models for information discovery", Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (2004)
- Zhang, J., "Expert finding in a social network. Advances in Databases : Concepts", Systems and Applications, pp.1066~1069 (2010)