首页    期刊浏览 2025年02月17日 星期一
登录注册

文章基本信息

  • 标题:A Supervised Method to Predict the Popularity of News Articles
  • 本地全文:下载
  • 作者:Ali Balali ; Masoud Asadpour ; Hesham Faili
  • 期刊名称:Computación y Sistemas
  • 印刷版ISSN:1405-5546
  • 出版年度:2017
  • 卷号:21
  • 期号:4
  • 页码:703-716
  • 语种:English
  • 出版社:Instituto Politécnico Nacional
  • 其他摘要:In this study, we identify the features of an article that encourage people to leave a comment for it. The volume of the received comments for a news article shows its importance. It also indirectly indicates the amount of influence a news article has on the public. Leaving comment on a news article indicates not only the visitor has read the article but also the article has been important to him/her. We propose a machine learning a pproach to predict the volume of comments using the information that is extracted about the users’ activities on the web pages of news agencies. In order to evaluate the proposed method, several experiments were performed. The results reveal salient improv ement in comparison with the baseline methods.
  • 其他关键词:Text mining; comments volume; content popularity; user behavior; social media.
国家哲学社会科学文献中心版权所有