Microblog Emergency Detection Model Based on Big Data Cluster Analysis

DOI

10.2991/978-94-6463-417-4_14How to use a DOI?

Keywords

Big data; Cluster analysis method; Microblog emergencies; Detection model

Abstract

This paper studies the microblog emergency detection model based on the big data cluster analysis method. A large number of users and relatively free speech information make the microblog a powerful tool that significantly influences society. Using the crawler designed for the page information structure of the microblog platform, the detection of emergencies in this paper takes the detection of event subject words as the clue. Firstly, the eigenvalues and data organization methods suitable for the microblog corpus are selected. Subsequently, the feature trajectories of each word in the time window are constructed. Combined with the time-domain and frequency-domain characteristics of the feature trajectories, the burst of words is determined. A nonlinear model of microblog attention is established, the data in key microblog articles is mined, and the correlation coefficient is determined. Additionally, we conducted the simulated experimental detection on the model. The result showed a significant improvement in clustering time cost and an improved detection efficiency of burst time.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yu Wang
AU  - Xiangming Kong
AU  - Nan Wang
PY  - 2024
DA  - 2024/05/07
TI  - Microblog Emergency Detection Model Based on Big Data Cluster Analysis
BT  - Proceedings of the 2024 5th International Conference on Big Data and Informatization Education (ICBDIE 2024)
PB  - Atlantis Press
SP  - 155
EP  - 166
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-417-4_14
DO  - 10.2991/978-94-6463-417-4_14
ID  - Wang2024
ER  -