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Identifying Topical Opinion Leaders in Social Community Question Answering

  • Tao Zhao
  • Hong Huang
  • Xiaoming Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Social community question answering (SCQA) sites not only provide regular question answering (QA) service but also form a social network where users can follow each other. Identifying topical opinion leaders who are both expert and influential in SCQA becomes a hot research topic. However, existing works focus on either using knowledge expertise to find experts for improving the quality of answers, or measuring user influence to identify influential ones. In this paper, we propose QALeaderRank, a topical opinion leader identification framework, incorporating both the topic-sensitive influence and the topical knowledge expertise. To measure a user’s topic-sensitive influence, we design a novel ranking algorithm that exploits both the social and QA features of SCQA, taking account of the network structure, topical similarity and knowledge authority. Besides, we incorporate three topic-relevant metrics to infer the topical expertise. Extensive experiments along with a user study demonstrate that QALeaderRank outperforms the compared state-of-the-art methods. QALeaderRank can also be used to identify multi-topic opinion leaders.

References

  1. 1.
    Bakshy, E., Hofman, J., Mason, W., et al.: Everyone’s an influencer: quantifying influence on Twitter. In: WSDM, pp. 65–74 (2011)Google Scholar
  2. 2.
    Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of Yahoo! answers. In: SIGKDD, pp. 866–874 (2008)Google Scholar
  3. 3.
    Bouguessa, M., Romdhane, L.B.: Identifying authorities in online communities. TIST 6(3), 30 (2015)CrossRefGoogle Scholar
  4. 4.
    Cha, M., Haddadi, H., Benevenuto, F., et al.: Measuring user influence in Twitter: the million follower fallacy. In: ICWSM, pp. 10–17 (2010)Google Scholar
  5. 5.
    Dwork, C., Kumar, R., Naor, M., et al.: Rank aggregation methods for the web. In: WWW, pp. 613–622 (2001)Google Scholar
  6. 6.
    Endres, D., Schindelin, J.: A new metric for probability distributions. IEEE TIT 49, 1858–1860 (2003)MathSciNetzbMATHGoogle Scholar
  7. 7.
    George, D.: SPSS for Windows Step by Step: A Simple Study Guide and Reference. Pearson Education India, Delhi (2011)Google Scholar
  8. 8.
    Ghosh, S., Sharma, N., Benevenuto, F., et al.: Cognos: crowdsourcing search for topic experts in microblogs. In: SIGIR, pp. 575–590 (2012)Google Scholar
  9. 9.
    Grin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)CrossRefGoogle Scholar
  10. 10.
    Haveliwala, T.: Topic-sensitive PageRank. In: WWW, pp. 517–526 (2002)Google Scholar
  11. 11.
    Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Lazarsfeld, P.F., Berelson, B., en Gaudet, H.: The people’s choice: how the voter makes up his mind in a presidential campaign. J. Consult. Psychol. 9(5), 268 (1968)Google Scholar
  13. 13.
    Lee, C., Kwak, H., Park, H., et al.: Finding influentials based on the temporal order of information adoption in Twitter. In: WWW, pp. 1137–1138 (2010)Google Scholar
  14. 14.
    Li, F., Du, T.: Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs. Dec. Support Syst. 51(1), 190–197 (2011)CrossRefGoogle Scholar
  15. 15.
    McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  16. 16.
    Miao, Q., Zhang, S., Meng, Y., et al.: Domain-sensitive opinion leader mining from online review communities. In: WWW, pp. 187–188 (2013)Google Scholar
  17. 17.
    Omari, A., Carmel, D., Rokhlenko, O., et al.: Novelty based ranking of human answers for community questions. In: SIGIR, pp. 215–224 (2016)Google Scholar
  18. 18.
    Pal, A., Konstan, J.: Expert identification in community question answering: exploring question selection bias. In: CIKM, pp. 1505–1508 (2010)Google Scholar
  19. 19.
    Riahi, F., Zolaktaf, Z., Shafiei, M., et al.: Finding expert users in community question answering. In: WWW, pp. 791–798 (2012)Google Scholar
  20. 20.
    Song, S., Tian, Y., Han, W., et al.: Leading users detecting model in professional community question answering services. In: GreenCom-iThings-CPSCom, pp. 1302–1307 (2013)Google Scholar
  21. 21.
    Wang, G., Gill, K., Mohanlal, M., et al.: Wisdom in the social crowd: an analysis of quora. In: WWW, pp. 1341–1352 (2013)Google Scholar
  22. 22.
    Weng, J., Lim, E., Jiang, J., et al.: TwitterRank: finding topic-sensitive influential twitterers. In: WSDM, pp. 261–270 (2010)Google Scholar
  23. 23.
    Zhai, Z., Xu, H., Jia, P.: Identifying opinion leaders in BBS. In: WI-IAT, pp. 398–401 (2008)Google Scholar
  24. 24.
    Zhang, J., Ackerman, M., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: WWW, pp. 221–230 (2007)Google Scholar
  25. 25.
    Zhao, Z., Zhang, L., He, X., et al.: Expert finding for question answering via graph regularized matrix completion. TKDE 27(4), 993–1004 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of GoettingenGoettingenGermany
  2. 2.School of Computer ScienceHuazhong University of Science and TechnologyWuhanChina

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