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Exploiting Content Quality and Question Difficulty in CQA Reputation Systems

  • Adrian Huna
  • Ivan SrbaEmail author
  • Maria Bielikova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9564)

Abstract

Community Question Answering (CQA) systems (e.g. StackOverflow) have gained popularity in the last years. With the increasing community size and amount of user generated content, a task of expert identification arose. To tackle this problem, various reputation mechanisms exist, however, they estimate user reputation especially according to overall user activity, while the quality of contributions is considered only secondary. As the result, reputation usually does not reflect the real value of users’ contributions and, moreover, some users (so called reputation collectors) purposefully abuse reputation systems to achieve a high reputation score. We propose a novel reputation mechanism that focuses primarily on the quality and difficulty of users’ contributions. Calculated reputation was compared with four baseline methods including the reputation schema employed in Stack Exchange platform. The experimental results showed a higher precision achieved by our approach, and confirmed an important role of contribution quality and difficulty in estimation of user reputation.

Keywords

Community Question Answering User reputation Expertise estimation 

Notes

Acknowledgment

This work was partially supported by grants. No. VG 1/0646/15, VG 1/0774/16 and KEGA 009STU-4/2014 and it is the partial result of collaboration within the SCOPES JRP/IP, No. 160480/2015.

References

  1. 1.
    Bosu, A., Corley, C.S., Heaton, D., Chatterji, D., Carver, J.C., Kraft, N.A.: Building reputation in stackoverflow: an empirical investigation. In: Proceedings of the 10th Working Conference on Mining Software Repositories, MSR 2013, pp. 89–92. IEEE Press, Piscataway (2013)Google Scholar
  2. 2.
    Hanrahan, B.V., Convertino, G., Nelson, L.: Modeling problem difficulty and expertise in stackoverflow. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work Companion, CSCW 2012, pp. 91–94. ACM, New York (2012)Google Scholar
  3. 3.
    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, CIKM 2007, pp. 919–922. ACM, New York (2007)Google Scholar
  4. 4.
    Liu, D.R., Chen, Y.H., Kao, W.C., Wang, H.W.: Integrating expert profile, reputation and link analysis for expert finding in question-answering websites. Inf. Process. Manage. 49(1), 312–329 (2013)CrossRefGoogle Scholar
  5. 5.
    Liu, J., Song, Y.I., Lin, C.Y.: Competition-based user expertise score estimation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 425–434. ACM, New York (2011)Google Scholar
  6. 6.
    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: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, pp. 886–893. ACM, New York (2013)Google Scholar
  7. 7.
    Paul, S.A., Hong, L., Chi, E.H.: Who is authoritative? understanding reputation mechanisms in quora. CoRR abs/1204.3724 (2012)Google Scholar
  8. 8.
    Srba, I., Bielikova, M.: Askalot: community question answering as a means for knowledge sharing in an educational organization. In: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work, CSCW 2015, pp. 179–182. ACM, New York (2015)Google Scholar
  9. 9.
    Srba, I., Bielikova, M.: Why stack overflow fails? preservation of sustainability in community question answering. IEEE Softw. (2015, accepted)Google Scholar
  10. 10.
    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
  11. 11.
    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, WWW 2007, pp. 221–230. ACM, New York (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Informatics and Information TechnologiesSlovak University of Technology in BratislavaBratislavaSlovakia

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