A Supervised Method to Predict the Popularity of News Articles

Authors

  • Ali Balali University of Tehran, School of ECE, College of Engineering, Tehran, Iran
  • Masoud Asadpour University of Tehran, School of ECE, College of Engineering, Tehran, Iran
  • Hesham Faili University of Tehran, School of ECE, College of Engineering, Tehran, Iran

DOI:

https://doi.org/10.13053/cys-21-4-2848

Keywords:

Text mining, comments volume, content popularity, user behavior, social media

Abstract

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 approach 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 improvement in comparison with the baseline methods.

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Published

2017-12-25

Issue

Vol. 21 No. 4 (2017): Advances in Human Language Technologies (Guest Editor: A. Gelbukh)

Section

Articles of the Thematic Issue

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