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Modeling and Predicting of News Popularity in Social Media Sources

Kemal Akyol1,*, Baha Şen2
Faculty of Engineering and Architecture, Kastamonu University, 37100, Kastamonu, Turkey.
Faculty of Engineering, Yıldırım Beyazıt University, 06500, Ankara, Turkey.
* Corresponding Author: Kemal Akyol. Email: .

Computers, Materials & Continua 2019, 61(1), 69-80. https://doi.org/10.32604/cmc.2019.08143

Abstract

The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets from a set of data are obtained within the frame of four categories: Economic, Microsoft, Obama and Palestine. Second, news popularity prediction in social network services is carried out by utilizing Gradient Boosted Trees, Multi-Layer Perceptron and Random Forest learning algorithms. The prediction performances of all algorithms are examined by considering Mean Absolute Error, Root Mean Squared Error and the R-squared evaluation metrics. The results show that most of the models designed by using these algorithms are proved to be applicable for this subject. Consequently, a comprehensive study for the news prediction is presented, using different techniques, drawing conclusions about the performances of algorithms in this study.

Keywords

News popularity, sentiment scores, social network services, Gradient Boosted Machines, Multi-Layer Perceptron, Random Forest

Cite This Article

K. Akyol and B. Şen, "Modeling and predicting of news popularity in social media sources," Computers, Materials & Continua, vol. 61, no.1, pp. 69–80, 2019.

Citations




This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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