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CCF Conference on Computer Supported Cooperative Work and Social Computing

ChineseCSCW 2019: Computer Supported Cooperative Work and Social Computing pp 335–350Cite as

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

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1042))

Abstract

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.

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References

  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). https://doi.org/10.1145/2488388.2488398

  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). https://doi.org/10.1145/1401890.1401994

  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). https://doi.org/10.1016/j.neucom.2018.01.034

    Article  Google Scholar 

  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. http://oro.open.ac.uk/40658/

  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). https://doi.org/10.1016/j.infoecopol.2017.08.001

    Article  Google Scholar 

  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). https://doi.org/10.1145/2020408.2020582

  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). https://doi.org/10.1145/2187980.2188207

  8. Fasano, G., Franceschini, A.: A multidimensional version of the Kolmogorov-Smirnov test. Mon. Not. R. Astron. Soc. 225(1), 155–170 (1987). https://doi.org/10.1093/mnras/225.1.155

    Article  Google Scholar 

  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. https://doi.org/10.1109/MSR.2013.6624007

  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). https://doi.org/10.1145/3148330.3148332

  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). https://doi.org/10.1016/j.im.2015.07.005. Novel applications of social media analytics

    Article  Google Scholar 

  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). https://doi.org/10.1145/1321440.1321575

  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). https://doi.org/10.1145/1871437.1871678

  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). https://doi.org/10.1145/2063576.2063885

  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). https://doi.org/10.1145/2505515.2505720

  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). https://doi.org/10.1145/2441776.2441918

  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). https://doi.org/10.1145/3187011

    Article  Google Scholar 

  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). https://doi.org/10.1002/asi.22750

    Article  Google Scholar 

  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). https://doi.org/10.1145/1518701.1518821

  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). https://doi.org/10.1007/978-3-642-22362-4_20

    Chapter  Google Scholar 

  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). https://doi.org/10.1145/2567948.2576965

  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). https://doi.org/10.1007/s10683-006-4309-2

    Article  Google Scholar 

  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). https://doi.org/10.1145/2934687

    Article  Google Scholar 

  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). http://dl.acm.org/citation.cfm?id=2017245.2017249

    Article  Google Scholar 

  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). https://doi.org/10.1002/asi.22913

    Article  Google Scholar 

  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. https://doi.org/10.1109/ASONAM.2014.6921702

  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). https://doi.org/10.1177/25.7.894009. Official Journal of the Histochemistry Society

    Article  Google Scholar 

  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). https://doi.org/10.1109/TPC.2011.2121830

    Article  Google Scholar 

  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). https://doi.org/10.1080/07421222.2016.1172459

    Article  Google Scholar 

  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). https://doi.org/10.1109/TKDE.2014.2356461

    Article  Google Scholar 

  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). https://doi.org/10.1145/2063576.2063931

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Authors and Affiliations

  1. China Agricultural University, Beijing, China

    Xiaoxue Shen, Adele Lu Jia & Ruizhi Sun

Authors
  1. Xiaoxue Shen
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  2. Adele Lu Jia
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  3. Ruizhi Sun
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Correspondence to Adele Lu Jia .

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Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

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Shen, X., Jia, A.L., Sun, R. (2019). Cultivating Online: Understanding Expert and Farmer Participation in an Agricultural Q&A Community. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_26

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  • DOI: https://doi.org/10.1007/978-981-15-1377-0_26

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  • Print ISBN: 978-981-15-1376-3

  • Online ISBN: 978-981-15-1377-0

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