Modeling and Predicting of News Popularity in Social Media Sources

被引:9
作者
Akyol, Kemal [1 ]
Sen, Baha [2 ]
机构
[1] Kastamonu Univ, Fac Engn & Architecture, TR-37100 Kastamonu, Turkey
[2] Yildirim Beyazit Univ, Fac Engn, TR-06500 Ankara, Turkey
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2019年 / 61卷 / 01期
关键词
News popularity; sentiment scores; social network services; Gradient Boosted Machines; Multi-Layer Perceptron; Random Forest;
D O I
10.32604/cmc.2019.08143
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:69 / 80
页数:12
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