A Supervised Method to Predict the Popularity of News Articles

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.

[1]  Azadeh Shakery,et al.  A learning approach for email conversation thread reconstruction , 2013, J. Inf. Sci..

[2]  Gilad Mishne,et al.  Leave a Reply: An Analysis of Weblog Comments , 2006 .

[3]  Yiannis Kompatsiaris,et al.  Predicting News Popularity by Mining Online Discussions , 2016, WWW.

[4]  Elizabeth M. Daly,et al.  Decomposing Discussion Forums and Boards Using User Roles , 2010, ICWSM.

[5]  Maarten de Rijke,et al.  News Comments: Exploring, Modeling, and Online Prediction , 2010, ECIR.

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Ee-Peng Lim,et al.  Comments-oriented blog summarization by sentence extraction , 2007, CIKM '07.

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  Maarten de Rijke,et al.  Extracting the discussion structure in comments on news-articles , 2007, WIDM '07.

[10]  Paulo Cortez,et al.  A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News , 2015, EPIA.

[11]  Gilad Mishne,et al.  Applied text analytics for blogs , 2007 .

[12]  Michelle Gumbrecht,et al.  Blogs as “Protected Space” , 2004 .

[13]  Mor Naaman,et al.  Topicality, time, and sentiment in online news comments , 2011, CHI EA '11.

[14]  Desney S. Tan,et al.  CHI '11 Extended Abstracts on Human Factors in Computing Systems , 2008, CHI 2011.

[15]  M. Asadpour,et al.  A Supervised Approach to Predict the Hierarchical Structure of Conversation Threads for Comments , 2014, TheScientificWorldJournal.

[16]  Wolfgang Nejdl,et al.  How useful are your comments?: analyzing and predicting youtube comments and comment ratings , 2010, WWW '10.

[17]  James Allan,et al.  Introduction to topic detection and tracking , 2002 .

[18]  Salman Jamali Comment Mining, Popularity Prediction, and Social Network Analysis , 2010 .

[19]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[20]  Serge Fdida,et al.  Predicting the popularity of online articles based on user comments , 2011, WIMS '11.

[21]  M. de Rijke,et al.  Predicting the volume of comments on online news stories , 2009, CIKM.

[22]  James Caverlee,et al.  Ranking Comments on the Social Web , 2009, 2009 International Conference on Computational Science and Engineering.

[23]  Rajeev Rastogi,et al.  Semi-supervised correction of biased comment ratings , 2012, WWW.

[24]  HE REN,et al.  Predicting and Evaluating the Popularity of Online News , 2015 .

[25]  Serge Fdida,et al.  A survey on predicting the popularity of web content , 2014, Journal of Internet Services and Applications.