Prediction of the dissemination of health news on microblogging sites based on ample feature selection and support vector machine

被引：1
|
作者：
Pei J. [1 ]
Shan P. [1 ]
机构：
[1] School of Business, Jiangnan University, Wuxi
来源：
Revue d'Intelligence Artificielle | 2019年 / 33卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Binary classification; Feature selection; News dissemination; Support vector machine (SVM);
D O I：
10.18280/ria.330505
中图分类号：
学科分类号：
摘要：
As a social networking service, microblogging sites provide an open platform facilitating the sharing and discussion of valuable news. This paper identifies the influencing factors of the dissemination of health news posts on Weibo, the leading microblogging site in China. The effects of these factors were tested with 863 news posts, all about public health issues. The content features, author features and a social feature of each post were evaluated, and collected into a set of ample, diverse features that characterize widely disseminated posts. In addition, the support vector machine (SVM) was adopted to differentiate between widely disseminated posts and normal posts, and compared with several classification methods through an experiment on microblog posts of health news. The results show that the SVM greatly outperformed the contrastive methods in predicting the dissemination trends of such news. The research results inform crisis managers about the public reaction towards specific news on public health issues, shedding important new light on news dissemination. © 2019 International Information and Engineering Technology Association. All rights reserved.
引用
收藏
页码：359 / 365
页数：6
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