Abstract
Community-based Question and Answering, or CQA, has been extensively studied nowadays. Studies focus on the characteristics on users help us understand the usage of the platforms, and pave the way for downstream tasks such as churn prediction, expert recommendation and spam detection. Different from previous works, we focus on analyzing the behavioral differences between different types of users, take the feedback of the community into consideration, and predict users’ future behaviors by their historical behaviors and feedback of the community. In this paper, we conduct a case study on Zhihu.com. We collect data of questions, answers, comments and users from Zhihu.com, make a brief analysis of the usage, divide the users into 3 groups named Takers, Devotees and Participants and study the differences between each type by KS tests, and conduct linear regressions to predict users’ future behaviors using their historical behaviors and feedback from the community. We find significant differences between each type of users, and find several positive correlations between users’ historical behaviors, feedback from the community and users’ future behaviors. By the analysis of users’ behaviors, our research provides a theoretical basis for the follow-up studies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xianzhi, W., Chaoran, H., Lina, Y., et al.: A survey on expert recommendation in community question answering. J. Comput. Sci. Technol. 33(4), 625–653 (2018)
Lin, X., Li, L., Zhai, Y., Qi, J.: UGC quality evaluation based on user communities and contents. In: 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE), pp. 1–5, Aalborg (2014). https://doi.org/10.1109/VITAE.2014.6934410
Reyyan, Y., Jamie, C.: Analyzing bias in CQA-based expert finding test sets. In: 37th international ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2014), pp. 967–970. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2600428.2609486
Grégoire, B., Paul, M., Yulan, H., Harith, A.: Predicting answering behaviour in online question answering communities. In: 26th ACM Conference on Hypertext & Social Media (HT 2015), pp. 201–210. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2700171.2791041
Haocheng, W., Wei, W., Ming, Z., Enhong, C., Lei, D., Heung-Yeung, S.: Improving search relevance for short queries in community question answering. In: 7th ACM International Conference on Web Search and Data Mining (WSDM 2014), pp. 43–52. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2556195.2556239
Yandong, L., Jiang, B., Eugene, A.: Predicting information seeker satisfaction in community question answering. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 483–490. Association for Computing Machinery, New York (2008). https://doi.org/10.1145/1390334.1390417
Xin, L., Yiqun, L., Rongjie, C., Shaoping, M.: Investigation of user search behavior while facing heterogeneous search services. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM 2017), pp. 161–170. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3018661.3018673
Kang, J., Yu, Z., Liang, Y., Xie, J., Guo, B.: Characterizing collective knowledge sharing behaviors in social network. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 869–876, Leicester, United Kingdom (2019). https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00178
Imrul, K., Nicolas, K., Daniele, Q., Adriana, I., Francesco, B.: Cultures in community question answering. In: 26th ACM Conference on Hypertext & Social Media (HT 2015), pp. 175–184. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2700171.2791034
Sun, Y., Guo, B., Li, Z., Cheng, J., Wang, L., Yu, Z.: Leveraging user profiling in click-through rate prediction based on Zhihu data. In: 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), pp. 131–136, Xi’an, China (2019)
Imrul, K., Nicolas, K., Francesco, B., Adriana, I.: Privacy concerns vs. user behavior in community question answering. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (ASONAM 2015), pp. 681–688. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2808797.2809422
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
Ivan, S., Maria, B.: A comprehensive survey and classification of approaches for community question answering. ACM Trans. Web 10(3), 63 (2016). https://doi.org/10.1145/2934687. Article id 18
Baoguo, Y., Suresh, M.: Exploring user expertise and descriptive ability in community question answering. In: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 320–327. IEEE Press, Beijing (2014). https://doi.org/10.1109/ASONAM.2014.6921604
Imrul, K., Nicolas, K., Daniele, Q., Adriana, I., Francesco, B.: The social world of content abusers in community question answering. In: 24th International Conference on World Wide Web (WWW 2015). International World Wide Web Conferences Steering Committee, pp. 570–580. Republic and Canton of Geneva, CHE (2015). https://doi.org/10.1145/2736277.2741674
Long, T.L., Chirag, S., Erik, C.: Bad users or bad content? Breaking the vicious cycle by finding struggling students in community question-answering. In: 2017 Conference on Conference Human Information Interaction and Retrieval (CHIIR 2017), pp. 165–174. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3020165.3020181
Gideon, D., Dan, P., Oleg, R., Idan, S: Churn prediction in new users of Yahoo! answers. In: 21st International Conference on World Wide Web (WWW 2012 Companion), pp. 829–834. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2187980.2188207
Chong, L., Eduarda, R., Gabriella, K., Nata, M., Aleksandar, I.: Model for voter scoring and best answer selection in community Q&A services. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (WI-IAT 2009), pp. 116–123. IEEE Computer Society, USA (2009). https://doi.org/10.1109/WI-IAT.2009.23
Zhimin, Z., Man, L., Zhengyu, N., Yue, L.: Exploiting user profile information for answer ranking in cQA. In: 21st International Conference on World Wide Web (WWW 2012 Companion), pp. 767–774. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2187980.2188199
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, W., Li, S., Rao, Y., Shen, X., Jia, L. (2021). An Analysis on User Behaviors in Online Question and Answering Communities. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_34
Download citation
DOI: https://doi.org/10.1007/978-981-16-2540-4_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2539-8
Online ISBN: 978-981-16-2540-4
eBook Packages: Computer ScienceComputer Science (R0)