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.

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References

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Published

2019-01-11

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