Abstract
With the rapid development of online social media platforms, online public opinion is becoming more and more complex. False news will not only reduce the credibility of the media, affect people’s value orientation, but also combat related industries. On the basis of combing the relevant theories of knowledge map and social network public opinion event topic map, this paper takes the hot event of ‘Rumors of illegal soaking antibacterial agent for Wuming fertile orange’ as an example, selects the data of related topics on Sina Weibo, extracts the entity and relationship of the topic map, and constructs the topic map construction process model. Neo4j is used to construct the topic map of hot events and analyze the evolution of public opinion, so as to provide suggestions for public opinion control.
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
References
Xu, Shi, Quan, et al.: https://mp.weixin.qq.com/s/bhk6iZdphif74HJlyUZOBQ. Last accessed 5 Jan 2020
Jun, Z.: Knowledge Graph. Higher Education Press, Beijing (2018)
Liu, J., Fang, R.: Research on the visualization of Nanyin characteristic resources based on topic maps. In: 3rd International Workshop on Materials Engineering and Computer Sciences (IWMECS), pp. 358–362. ACSR-Advances in Comptuer Science Research, Jinan (2018)
Marrone, M., Linnenluecke, M.: Interdisciplinary research maps: a new technique for visualizing research topics. PLOS ONE 15(11) (2020)
Mi, Y., Moon, S., Ryu, G.: A topic map keyword extraction technique using dietary dessert cafe selection attributes and a recommendation system based on wide deep learning. Soc. Converg. Knowl. Trans. 9(2), 11–24 (2021)
Zhou, J., Fan, Y., Zhang, J.: Generating knowledge maps for songs and users in music market with probabilistic topic model. In: 5th IEEE International Conference on Big Data Computing Service and Applications, pp. 83–92. IEEE Computer Soc, San Francisco (2019)
Yang, H.: Learning topic map from large scale social media data. In: 29th World Wide Web Conference (WWW), pp. 279–283. Assoc Computing Machinery, Taipei (2020)
Zheng, H.: Research on the Construction Method of Character Graph Based on Microblog. Xihua University, Chengdu (2017)
Li, D., Zhang, Y., et al.: Overview of entity relation extraction methods. Comput. Res. Dev. 57(07), 1424–1448 (2020)
Wang, C., Xu, J., Zhang, Y.: Summary of entity relation extraction. Comput. Eng. Appl. 56(12), 25–36 (2020)
Acknowledgement
Work described in this paper was funded by the National Natural Science Foundation of China under Grant No.71671093, and Jiangsu Provincial Graduate Research and Innovation Program No.KYCX20_0832.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, W., Wang, Y., Huang, J., Cheng, X. (2022). Visualization Analysis of Hot Event Propagation Topic Map. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-23741-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23740-9
Online ISBN: 978-3-031-23741-6
eBook Packages: Computer ScienceComputer Science (R0)