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Computación y Sistemas

Print version ISSN 1405-5546

Abstract

BALALI, Ali; ASADPOUR, Masoud  and  FAILI, Hesham. A Supervised Method to Predict the Popularity of News Articles. Comp. y Sist. [online]. 2017, vol.21, n.4, pp.703-716. ISSN 1405-5546.  http://dx.doi.org/10.13053/cys-21-4-2848.

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.

Keywords : Text mining; comments volume; content popularity; user behavior; social media.

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