A Comprehensive Review on Online News Popularity Prediction using Machine Learning Approach

Authors

  • Priyanka Rathord M.Tech. Scholar Department of CSE Radharaman Institute of Technology & Science Bhopal, M.P, India
  • Dr. Anurag Jain Professor Department of CSE Radharaman Institute of Technology & Science Bhopal. M.P, India
  • Chetan Agrawal Assistant Professor Department of CSE Radharaman Institute of Technology & Science Bhopal. M.P, India

DOI:

https://doi.org/10.24113/ijoscience.v5i1.181

Abstract

With the help of Internet, the online news can be instantly spread around the world. Most of peoples now have the habit of reading and sharing news online, for instance, using social media like Twitter and Facebook. Typically, the news popularity can be indicated by the number of reads, likes or shares. For the online news stake holders such as content providers or advertisers, it’s very valuable if the popularity of the news articles can be accurately predicted prior to the publication. Thus, it is interesting and meaningful to use the machine learning techniques to predict the popularity of online news articles. Various works have been done in prediction of online news popularity. Popularity of news depends upon various features like sharing of online news on social media, comments of visitors for news, likes for news articles etc. It is necessary to know what makes one online news article more popular than another article. Unpopular articles need to get optimize for further popularity. In this paper, different methodologies are analyzed which predict the popularity of online news articles. These methodologies are compared, their parameters are considered and improvements are suggested. The proposed methodology describes online news popularity predicting system.

Downloads

Download data is not yet available.

References

[1] Kelwin Fernandes, Pedro Vinagre, and Paulo Cortez, “A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News” EPIA 2015, pp. 535–546, 2015.
[2] He Ren, Quan Yang, Stanford University, “Predicting and Evaluating the Popularity of Online News”, Machine Learning Project Work Report, 2015, pp. 1-5.
[3] Al-Mutairi, Hanadi Muqbil, and Mohammad Badruddin Khan. “Predicting the Popularity of Trending Arabic Wikipedia Articles Based on External Stimulants Using Data/Text Mining Techniques.” Cloud Computing (ICCC), 2015 International Conference on. IEEE, 2015.
[4] Elena Hensinger, Ilias Flaounas, Nello Cristianini, “Modelling and predicting news popularity” Springer, Pattern Anal Applic, 2013, pp. 623–635.
[5] Tatar Alexandru, et al. “Predicting the popularity of online articles based on user comments.” Proceedings of the International Conference on Web Intelligence, Mining and Semantics. ACM, 2011.
[6] Bandari Roja, Sitaram Asur, and Bernardo A. Huberman. “The pulse of news in social media: Forecasting popularity.” arXiv preprint arXiv:1202.0332 (2012).
[7] Arapakis, Ioannis, B. Barla Cambazoglu, and Mounia Lalmas. “On the feasibility of predicting news popularity at cold start.” International Conference on Social Informatics. Springer International Publishing, 2014.
[8] Castillo, Carlos, et al. “Characterizing the life cycle of online news stories using social media reactions.” Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. ACM, 2014.
[9] Szabo Gabor, and Bernardo A. Huberman. “Predicting the popularity of online content.” Communications of the ACM 53.8 (2010): 80–88.
[10] Lee Jong Gun, Sue Moon, and Kave Salamatian. “An approach to model and predict the popularity of online contents with explanatory factors.” Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on. Vol. 1. IEEE, 2010.
[11] Bo Wu, Haiying Shen, “Analyzing and Predicting News Popularity on Twitter”, International Journal of Information Management, Elsevier, 21st July 2015, pp. 702-711.
[12] Zhiyi Tan, Yanfeng Wang, Ya Zhang, and Jun Zhou, “A Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videos”, IEEE Transactions on Broadcasting, 2016.
[13] Lee Jong Gun, Sue Moon, and Kave Salamatian. “An approach to model and predict the popularity of online contents with explanatory factors.” Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE, 2010.
[14] Swati Choudhary, Angkirat Singh Sandhu and Tribikram Pradhan, “Genetic Algorithm Based Correlation Enhanced Prediction of Online News Popularity” Computational Intelligence in Data Mining, Advances in Intelligent Systems and Computing, Springer, 2017, pp.133-144.

Downloads

Published

01/11/2019

How to Cite

Rathord, P., Jain, D. A., & Agrawal, C. (2019). A Comprehensive Review on Online News Popularity Prediction using Machine Learning Approach. SMART MOVES JOURNAL IJOSCIENCE, 5(1), 25–29. https://doi.org/10.24113/ijoscience.v5i1.181

Issue

Vol. 5 No. 1 (2019): Volume 5, Issue 1, January 2019

Section

Articles

License

Copyright (c) 2019 Priyanka Rathord, Dr. Anurag Jain, Chetan Agrawal

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms. 

Under the following terms:

  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.