Abstract
Using artificial intelligence method to analyze the data can help enterprises accurately predict community development. From the point of view of the behavior of community users, this paper uses artificial intelligence to define the behavior attributes of users, summarize the behavior patterns of users, use the interaction between users as the edge of online community network, and use the size of user groups as the sub-network of online community network. We analyze the behavior of community users, and make irregular changes in the online community network. Prediction is based on the evolution model of an online community network. In the process of experiment, we use artificial intelligence to extract the characteristics of user behavior and realize the grouping of user groups. The designed evolutionary model can predict the behavior of community users and the interaction of community users. The simulated evolutionary structure of the community is similar to that of real community network.
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
Chai, J.X., Fan, K.K.: Mobile inverted constructivism: education of interaction technology in social media. EURASIA J. Math. Sci. Technol. Educ. 12(7) (2016)
Chen, Y., Zhen, F., Bo, W., Wei, Z.: A study of internet information asymmetry relations among Chinese cities based on the micro-blog platform. Adv. Earth Sci. 27(12), 1353–1362 (2012)
Sigurdsson, S.: Blog life: dynamics of cats. Phys. World 21(2), 35 (2015)
Alexander-Bloch, A., Lambiotte, R., Roberts, B., Giedd, J., Gogtay, N., Bullmore, E.: The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. Neuroimage 59(4), 3889–3900 (2012)
Derthick, K., Scott, J., Villar, N., Winkler, C.: Exploring smartphone-based web user interfaces for appliances. In: International Conference on Human-Computer Interaction with Mobile Devices & Services. ACM (2013)
Zinovyev, A., Mirkes, E.: Data complexity measured by principal graphs. Comput. Math. Appl. 65(10), 1471–1482 (2013)
Hua, G., Haughton, D.: A network analysis of an online expertise sharing community. Soc. Netw. Anal. Min. 2(4), 291–303 (2012)
Celebi, M.E., Kingravi, H.A.: Deterministic initialization of the k-means algorithm using hierarchical clustering. Int. J. Pattern Recognit. Artif. Intell. 26(07), 1250018-1–1250018-25 (2013)
Ostrovsky, R., Rabani, Y., Schulman, L.J., Swamy, C.: The effectiveness of Lloyd-type methods for the k-means problem. J. ACM 59(6), 165–176 (2013)
Acknowledgement
The authors would like to thank for financial support by youth fund project of the humanities and social sciences of Education Ministry (15YJC870004), science and technology innovation team of XiangNan University, Hunan province undergraduate research-based learning and innovative experimental project (719), Big data research institute of XiangNan University, and social science planning project of Chenzhou (Czsskl2017067).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, XG., Lu, Rh., Duan, S., Wang, Ld. (2020). Analysis of Big Data of an Online Community Based on Artificial Intelligence. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_109
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_109
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)