Network Public Opinion Analysis of the Top 100 Events of 2022 Based on K-Means Clustering

Zizhou Fang

doi:10.62051/s9qr9w81

Authors

Zizhou Fang

DOI:

https://doi.org/10.62051/s9qr9w81

Keywords:

Online Public Opinion; Opinion Indicators; Top 100 Events of 2022; SPSS; K-means Clustering.

Abstract

To explore the intrinsic patterns and characteristics of the generation and dissemination of online public opinion in a big data environment, this paper collected the top 100 most influential and high-ranking public events from 2022. After quantifying these events with relevant indicators, we employed the classic K-means clustering algorithm in SPSS to perform a cluster analysis on these high-impact 2022 events. This analysis yielded several distinct categories of high-interest online public opinion clusters. We then examined and summarized the unique characteristics of each type, offering a novel perspective for identifying and classifying significant online public opinion events. Additionally, this study provides constructive recommendations to help the public better navigate major online public opinion dynamics.

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