International Conference on User Modeling, Adaptation, and Personalization

UMAP 2015: User Modeling, Adaptation and Personalization pp 315-327 | Cite as

Harnessing Engagement for Knowledge Creation Acceleration in Collaborative Q&A Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)

Abstract

Thanks to reputation and gamification mechanisms, collaborative question answering systems coordinate the process of topical knowledge creation of thousands of users. While successful, these systems face many challenges: on one hand, the volume of submitted questions overgrows the amount of new users willing, and capable, of answering them. On the other hand, existing users need to be retained and optimally allocated. Previous work demonstrates the positive effects that two important aspects, namely engagement and expertise valorisation, can have on user quality and quantity of participation. The magnitude of their effect can greatly vary across users and across topics. In this paper we advocate for a more in-depth study of the interplay that exists between user engagement factors in question answering systems. Our working hypothesis is that the process of knowledge creation can be accelerated by better understanding and exploiting the combined effects of the interests and expertise of users, with their intrinsic and extrinsic motivations. We perform a study over 6 years of data from the StackOverflow platform. By defining metrics of expertise and (intrinsic and extrinsic) motivations, we show how they distribute and correlate across platform’s users and topics. By means of an off-line question routing experiment, we show how topic-specific combinations of motivations and expertise can help accelerating the knowledge creation process.

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References

  1. 1.
    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: KDD 2012, pp. 850–858. ACM (2012)Google Scholar
  2. 2.
    Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Steering user behavior with badges. In: WWW 2013, pp. 95–106. ACM (2013)Google Scholar
  3. 3.
    Attfield, S., Kazai, G., Lalmas, M., Piwowarski, B.: Towards a science of user engagement. In: WSDM Workshop on User Modelling for Web Applications (2011)Google Scholar
  4. 4.
    Bosu, A., Corley, C.S., Heaton, D., Chatterji, D., Carver, J.C., Kraft, N.A.: Building reputation in stackoverflow: an empirical investigation. In: MSR 2013, pp. 89–92. IEEE (2013)Google Scholar
  5. 5.
    Chang, S., Pal, A.: Routing questions for collaborative answering in community question answering. In: ASONAM 2013, pp. 494–501. IEEE/ACM (2013)Google Scholar
  6. 6.
    Deci, E., Ryan, R.M.: Self-determination Theory. Handbook of Theories of Social Psychology 1, 416–433 (2008)Google Scholar
  7. 7.
    Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R.: The Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press (2006)Google Scholar
  8. 8.
    Guo, J., Xu, S., Bao, S., Yu, Y.: Tapping on the potential of q&a community by recommending answer providers. In: CIKM 2008, pp. 921–930. ACM (2008)Google Scholar
  9. 9.
    Guy, I., Avraham, U., Carmel, D., Ur, S., Jacovi, M., Ronen, I.: Mining expertise and interests from social media. In: WWW 2013, pp. 515–526. ACM (2013)Google Scholar
  10. 10.
    Järvelin, K., Kekäläinen, J.: Cumulated Gain-based Evaluation of IR Techniques. ACM Transactions on Information Systems 20(4), 422–446 (2002)CrossRefGoogle Scholar
  11. 11.
    Liu, J., Song, Y.-I., Lin, C.-Y.: Competition-based user expertise score estimation. In: SIGIR 2011, pp. 425–434. ACM (2011)Google Scholar
  12. 12.
    Nam, K.K., Ackerman, M.S., Adamic, L.A.: Questions in, knowledge in?: a study of naver’s question answering community. In: SIGCHI 2009, pp. 779–788. ACM (2009)Google Scholar
  13. 13.
    Pal, A., Farzan, R., Konstan, J.A., Kraut, R.E.: Early detection of potential experts in question answering communities. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 231–242. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  14. 14.
    Pal, A., Wang, F., Zhou, M.X., Nichols, J., Smith, B.A.: Question routing to user communities. In: CIKM 2013, pp. 2357–2362. ACM (2013)Google Scholar
  15. 15.
    Ravi, S., Pang, B., Rastogi, V., Kumar, R.: Great question! question quality in community q&a. In: ICWSM 2014, pp. 426–435. AAAI (2014)Google Scholar
  16. 16.
    Yang, J., Hauff, C., Bozzon, A., Houben, G.J.: Asking the right question in collaborative q&a systems. In: Hypertext 2014, pp. 179–189. ACM (2014)Google Scholar
  17. 17.
    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) Google Scholar
  18. 18.
    Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., Chen, Z.: CQARank: jointly model topics and expertise in community question answering. In: CIKM 2013, pp. 99–108. ACM (2013)Google Scholar
  19. 19.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: WWW 2007, pp. 221–230. ACM (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands

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