sis 24(3):

Research Article

Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm

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  • @ARTICLE{10.4108/eetsis.4873,
        author={Zhenghan Gao and Anzhu Zheng},
        title={Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={3},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={2},
        keywords={new media event warning methods, K-means clustering algorithm, seasonal optimization algorithm, random forests},
        doi={10.4108/eetsis.4873}
    }
    
  • Zhenghan Gao
    Anzhu Zheng
    Year: 2024
    Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm
    SIS
    EAI
    DOI: 10.4108/eetsis.4873
Zhenghan Gao1,*, Anzhu Zheng2
  • 1: Qingdao University of Technology
  • 2: Shandong University
*Contact email: gaozhenghan199311@163.com

Abstract

INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.