Cultivating Online: Understanding Expert and Farmer Participation in an Agricultural Q&A Community

  • Xiaoxue Shen
  • Adele Lu JiaEmail author
  • Ruizhi Sun
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)


Nowadays, due to the shortage of offline agricultural experts, hundreds of thousands of farmers in China seek online for cultivation advices. A key design issue here is to provide users with timely and high quality answers, which, for general Community-based Question and Answering (CQA) platforms such as Quora, has been extensively studied before. However, answering questions raised in agricultural CQA platforms requires domain knowledge and professional experience, and users are expected to behave differently. In this article, we conduct a case study on a agricultural CQA platform named Farm-Doctor. We obtain the whole knowledge repository of Farm-Doctor, we investigate the behavioral differences between experts and farmers, and we analyze the factors that influence the users’ answering behavior. Our results show that there exists obvious behavioral differences between experts and farmers, and differences also exist in the factors that influence user’s answering behavior. While recognition, i.e., whether answers are well received by the community, has a positive impact on the answering behavior of both experts and farmers, reciprocation, i.e., how their own questions are treated, works differently. Experts acknowledge the “effort” of the community, i.e., the number of answers they get for their own questions has a positive effect on the number of answers they provide whereas the quality of the answers does not. Farmers, on the other hand, care both the number and the quality of the answers. Our research provides valuable information for the community to motivate the users to answer questions and therefore helps farmers solving their cultivation problems, in hoping to improve their lives.


Community-based Question and Answering Agriculture CQA Behavioral analysis Linear regression 


  1. 1.
    Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Steering user behavior with badges. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 95–106. ACM, New York (2013).
  2. 2.
    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, KDD 2008, pp. 866–874. ACM, New York (2008).
  3. 3.
    Chen, Z., Zhang, C., Zhao, Z., Yao, C., Cai, D.: Question retrieval for community-based question answering via heterogeneous social influential network. Neurocomputing 285, 117–124 (2018). Scholar
  4. 4.
    Choudhury, S., Alani, H.: Exploring user behavior and needs in Q & A communities. In: Rospigliosi, A., Greener, S. (eds.) Proceedings of the European Conference on Social Media: ECSM 2014, pp. 80–89. Academic Conferences and Publishing International Limited, July 2014.
  5. 5.
    DeVaro, J., Kim, J.H., Wagman, L., Wolff, R.: Motivation and performance of user-contributors: evidence from a CQA forum. Inf. Econ. Policy 42, 56–65 (2018). Scholar
  6. 6.
    Dror, G., Koren, Y., Maarek, Y., Szpektor, I.: I want to answer; who has a question?: Yahoo! answers recommender system. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 1109–1117. ACM, New York (2011).
  7. 7.
    Dror, G., Pelleg, D., Rokhlenko, O., Szpektor, I.: Churn prediction in new users of Yahoo! answers. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012 Companion, pp. 829–834. ACM, New York (2012).
  8. 8.
    Fasano, G., Franceschini, A.: A multidimensional version of the Kolmogorov-Smirnov test. Mon. Not. R. Astron. Soc. 225(1), 155–170 (1987). Scholar
  9. 9.
    Grant, S., Betts, B.: Encouraging user behaviour with achievements: an empirical study. In: 2013 10th Working Conference on Mining Software Repositories (MSR), pp. 65–68, May 2013.
  10. 10.
    Jan, S.T., Wang, C., Zhang, Q., Wang, G.: Pay-per-question: towards targeted Q & A with payments. In: Proceedings of the 2018 ACM Conference on Supporting Groupwork, GROUP 2018, pp. 1–11. ACM, New York (2018).
  11. 11.
    Jin, J., Li, Y., Zhong, X., Zhai, L.: Why users contribute knowledge to online communities: an empirical study of an online social Q & A community. Inf. Manag. 52(7), 840–849 (2015). Novel applications of social media analyticsCrossRefGoogle Scholar
  12. 12.
    Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, pp. 919–922. ACM, New York (2007).
  13. 13.
    Li, B., King, I.: Routing questions to appropriate answerers in community question answering services. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1585–1588. ACM, New York (2010).
  14. 14.
    Li, B., King, I., Lyu, M.R.: Question routing in community question answering: putting category in its place. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2041–2044. ACM, New York (2011).
  15. 15.
    Yang, L., et al.: CQArank: jointly model topics and expertise in community question answering. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM 2013, pp. 99–108. ACM, New York (2013).
  16. 16.
    Liu, Z., Jansen, B.J.: Factors influencing the response rate in social question and answering behavior. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, CSCW 2013, pp. 1263–1274. ACM, New York (2013).
  17. 17.
    Liu, Z., Xia, Y., Liu, Q., He, Q., Zhang, C., Zimmermann, R.: Toward personalized activity level prediction in community question answering websites. ACM Trans. Multimedia Comput. Commun. Appl. 14(2s), 41:1–41:15 (2018). Scholar
  18. 18.
    Lou, J., Fang, Y., Lim, K.H., Peng, J.Z.: Contributing high quantity and quality knowledge to online Q & A communities. J. Am. Soc. Inf. Sci. Technol. 64(2), 356–371 (2013). Scholar
  19. 19.
    Nam, K.K., Ackerman, M.S., Adamic, L.A.: Questions in, knowledge in?: a study of Naver’s question answering community. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 779–788. ACM, New York (2009).
  20. 20.
    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). Scholar
  21. 21.
    Pudipeddi, J.S., Akoglu, L., Tong, H.: User churn in focused question answering sites: characterizations and prediction. In: Proceedings of the 23rd International Conference on World Wide Web, WWW 2014 Companion, pp. 469–474. ACM, New York (2014).
  22. 22.
    Resnick, P., Zeckhauser, R., Swanson, J., Lockwood, K.: The value of reputation on eBay: a controlled experiment. Exp. Econ. 9(2), 79–101 (2006). Scholar
  23. 23.
    Srba, I., Bielikova, M.: A comprehensive survey and classification of approaches for community question answering. ACM Trans. Web 10(3), 18:1–18:63 (2016). Scholar
  24. 24.
    Wasko, M.M., Faraj, S.: Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Q. 29(1), 35–57 (2005). Scholar
  25. 25.
    Wu, P.F., Korfiatis, N.: You scratch someone’s back and we’ll scratch yours: collective reciprocity in social Q & A communities. J. Assoc. Inf. Sci. Technol. 64(10), 2069–2077 (2013). Scholar
  26. 26.
    Yang, B., Manandhar, S.: Tag-based expert recommendation in community question answering. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 960–963, August 2014.
  27. 27.
    Young, I.: Proof without prejudice: use of the kolmogorov-smirnov test for the analysis of histograms from flow systems and other sources. J. Histochem. Cytochem. 25(7), 935–941 (1977). Official Journal of the Histochemistry SocietyCrossRefGoogle Scholar
  28. 28.
    Yu, J., Jiang, Z., Chan, H.C.: The influence of sociotechnological mechanisms on individual motivation toward knowledge contribution in problem-solving virtual communities. IEEE Trans. Prof. Commun. 54(2), 152–167 (2011). Scholar
  29. 29.
    Zhao, L., Detlor, B., Connelly, C.E.: Sharing knowledge in social Q&A sites: the unintended consequences of extrinsic motivation. J. Manag. Inf. Syst. 33(1), 70–100 (2016). Scholar
  30. 30.
    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). Scholar
  31. 31.
    Zhu, H., Cao, H., Xiong, H., Chen, E., Tian, J.: Towards expert finding by leveraging relevant categories in authority ranking. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2221–2224. ACM, New York (2011).

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.China Agricultural UniversityBeijingChina

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