Expert Finding in Citizen Science Platform for Biodiversity Monitoring via Weighted PageRank Algorithm

  • Zakaria SaoudEmail author
  • Colin Fontaine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11191)


Numerous citizen science platforms aiming at monitoring biodiversity have emerged in the recent years. These platforms collect biodiversity data from participants and allow them to increase their scientific knowledge and share it with other participants, experts and scientists. One key aspect of such platforms is quality control on the data, a task usually performed by a limited number of co-opted experts. With the amount of data collected increasing steeply, finding new experts is needed. In this paper we propose a new graph-based expert finding approach for the citizen science platform SPIPOLL, aiming at collecting data on pollinator diversity across France. We exploit both users comments quality and users social relations to calculate users expertise for specific insect family. Experimental results show that the proposed method performs better than the state-of-the-art expert finding algorithms.


Expert finding PageRank algorithm Citizen sciences 


  1. 1.
    Aktas, M.S., Nacar, M.A., Menczer, F.: Using hyperlink features to personalize web search. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds.) WebKDD 2004. LNCS (LNAI), vol. 3932, pp. 104–115. Springer, Heidelberg (2006). Scholar
  2. 2.
    Aslay, Ç., O’Hare, N., Aiello, L.M., Jaimes, A.: Competition-based networks for expert finding. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1033–1036. ACM (2013)Google Scholar
  3. 3.
    Becchetti, L., Castillo, C., Donato, D., Leonardi, S., Baeza-Yates, R.A.: Link-based characterization and detection of web spam. In: AIRWeb, pp. 1–8 (2006)Google Scholar
  4. 4.
    Cai, Y., Chakravarthy, S.: Expertise ranking of users inQA community. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7825, pp. 25–40. Springer, Heidelberg (2013). Scholar
  5. 5.
    Dom, B., Eiron, I., Cozzi, A., Zhang, Y.: Graph-based ranking algorithms for e-mail expertise analysis. In: Proceedings of the 8th ACM SIGMOD workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 42–48. ACM (2003)Google Scholar
  6. 6.
    Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.: Combating web spam with trustrank. In: Proceeding of the Thirtieth international conference on very large date bases-Vol. 30, pp. 576–587. VLDB Endowment (2004)Google Scholar
  7. 7.
    Huang, C., Yao, L., Wang, X., Benatallah, B., Sheng, Q.Z.: Expert as a service: software expert recommendation via knowledge domain embeddings in stack overflow. In: 2017 IEEE International Conference on Web Services (ICWS), pp. 317–324. IEEE (2017)Google Scholar
  8. 8.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Li, Y., Ma, S., Zhang, Y., Huang, R.: Expertise network discovery via topic and link analysis in online communities. In: 2012 IEEE 12th International Conference on Advanced Learning Technologies (ICALT), pp. 311–315. IEEE (2012)Google Scholar
  10. 10.
    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, pp. 425–434. ACM (2011)Google Scholar
  11. 11.
    Liu, X., Bollen, J., Nelson, M.L., Van de Sompel, H.: Co-authorship networks in the digital library research community. Inf. Process. Manag. 41(6), 1462–1480 (2005)CrossRefGoogle Scholar
  12. 12.
    Mihalcea, R.: Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 411–418. Association for Computational Linguistics (2005)Google Scholar
  13. 13.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)Google Scholar
  14. 14.
    Procaci, T.B., Siqueira, S.W.M., Braz, M.H.L.B., de Andrade, L.C.V.: How to find people who can help to answer a question?-analyses of metrics and machine learning in online communities. Comput. Human Behav. 51, 664–673 (2015)CrossRefGoogle Scholar
  15. 15.
    Rafiei, M., Kardan, A.A.: A novel method for expert finding in online communities based on concept map and pagerank. Human-centric Comput. Inf. Sci. 5(1), 10 (2015)CrossRefGoogle Scholar
  16. 16.
    San Pedro, J., Karatzoglou, A.: Question recommendation for collaborative question answering systems with rankslda. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 193–200. ACM (2014)Google Scholar
  17. 17.
    Shen, J., Shen, W., Fan, X.: Recommending experts in q & a communities by weighted hits algorithm. In: Information Technology and Applications, 2009. IFITA’09. International Forum on. vol. 2, pp. 151–154. IEEE (2009)Google Scholar
  18. 18.
    Wei, C.P., Lin, W.B., Chen, H.C., An, W.Y., Yeh, W.C.: Finding experts in online forums for enhancing knowledge sharing and accessibility. Comput. Human Behav. 51, 325–335 (2015)CrossRefGoogle Scholar
  19. 19.
    Xing, W., Ghorbani, A.: Weighted pagerank algorithm. In: Proceedings Second Annual Conference on Communication Networks and Services Research, 2004, pp. 305–314. IEEE (2004)Google Scholar
  20. 20.
    Yeniterzi, R., Callan, J.: Constructing effective and efficient topic-specific authority networks for expert finding in social media. In: Proceedings of the First International Workshop on Social Media Retrieval and Analysis, pp. 45–50. ACM (2014)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    Zhao, Z., Yang, Q., Cai, D., He, X., Zhuang, Y.: Expert finding for community-based question answering via ranking metric network learning. In: IJCAI, pp. 3000–3006 (2016)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    Zhou, Z.M., Lan, M., Niu, Z.Y., Lu, Y.: Exploiting user profile information for answer ranking in cqa. In: Proceedings of the 21st International Conference on World Wide Web, pp. 767–774. ACM (2012)Google Scholar
  25. 25.
    Zhu, H., Chen, E., Xiong, H., Cao, H., Tian, J.: Ranking user authority with relevant knowledge categories for expert finding. World Wide Web 17(5), 1081–1107 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Centre dEcologie et des Science de la Conservation, UMR 7204 CNRS-MNHN-SU, Musum national d’Histoire naturelleParisFrance

Personalised recommendations