Abstract
Question answering (Q&A) communities are becoming important repositories of crowd-generated knowledge. The success of these communities mainly depends on the contribution of experts, who provide a significant number of high quality answers. Identifying these experts as soon as they participate in a community enables the community managers to nurture and retain experts. However, there is a great challenge to complete this task because lack of enough activities during users’ early participation. To take full advantage of users’ limited activities, we study the evolution of users’ temporal behavior that indicates deeper insights of the activities, both the absolute view and the relative view. Based on our analysis, we propose a Temporal Behavior Model to identify potential experts. Experiments on a large online Q&A community prove that our model can be combined with previous researches to improve the identification performance even further.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Adamic, L.A., Zhang, J., Bakshy, E., Ackerman, M.S.: Knowledge sharing and yahoo answers: everyone knows something. In: Proceedings of the 17th International Conference on World Wide Web, pp. 665–674. ACM (2008)
Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Discovering value from community activity on focused question answering sites: a case study of stack overflow. In: Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining, pp. 850–858. ACM (2012)
Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of yahoo! answers. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 866–874. ACM (2008)
Cavusoglu, H., Li, Z., Huang, K.W.: Can gamification motivate voluntary contributions?: the case of stackoverflow Q&A community. In: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing, pp. 171–174. ACM (2015)
Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 919–922. ACM (2007)
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N.S., Hurst, M.: Patterns of cascading behavior in large blog graphs. In: SIAM International Conference on Data Mining, vol. 7, pp. 551–556. SIAM (2007)
Mamykina, L., Manoim, B., Mittal, M., Hripcsak, G., Hartmann, B.: Design lessons from the fastest Q&A site in the west. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2857–2866. ACM (2011)
Movshovitz-Attias, D., Movshovitz-Attias, Y., Steenkiste, P., Faloutsos, C.: Analysis of the reputation system and user contributions on a question answering website: stackoverflow. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 886–893. IEEE (2013)
Pal, A., Chang, S., Konstan, J.A.: Evolution of experts in question answering communities. In: International Conference on Web and Social Media (2012)
Pal, A., Harper, F.M., Konstan, J.A.: Exploring question selection bias to identify experts and potential experts in community question answering. ACM Trans. Inf. Syst. 30(2), 10 (2012)
Pal, A., Konstan, J.A.: Expert identification in community question answering: exploring question selection bias. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1505–1508. ACM (2010)
Pudipeddi, J.S., Akoglu, L., Tong, H.: User churn in focused question answering sites: characterizations and prediction. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp. 469–474 (2014)
Wang, G.A., Jiao, J., Abrahams, A.S., Fan, W., Zhang, Z.: Expertrank: a topic-aware expert finding algorithm for online knowledge communities. Decis. Support Syst. 54(3), 1442–1451 (2013)
Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
Yang, J., Tao, K., Bozzon, A., Houben, G.-J.: Sparrows and owls: characterisation of expert behaviour in stackoverflow. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 266–277. Springer, Heidelberg (2014)
Yeniterzi, R., Callan, J.: Analyzing bias in CQA-based expert finding test sets. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 967–970. ACM (2014)
Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on World Wide Web, pp. 221–230. ACM (2007)
Zhao, Z., Zhang, L., He, X., Ng, W.: Expert finding for question answering via graph regularized matrix completion. IEEE Trans. Knowl. Data Eng. 27(4), 993–1004 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Fu, M., Zhu, M., Su, Y., Zhu, Q., Li, M. (2016). Modeling Temporal Behavior to Identify Potential Experts in Question Answering Communities. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2016. Lecture Notes in Computer Science(), vol 9929. Springer, Cham. https://doi.org/10.1007/978-3-319-46771-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-46771-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46770-2
Online ISBN: 978-3-319-46771-9
eBook Packages: Computer ScienceComputer Science (R0)