Modeling and Predicting of News Popularity in Social Media Sources
Yazarlar (2)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
Baha Şen
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Computers Materials and Continua (Q1) | ||
| Dergi ISSN | 1546-2218 Dergi Bilgileri (2019) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 10-2019 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2019 |
| Cilt / Sayı / Sayfa | 61 / 1 / 69–80 | DOI | 10.32604/cmc.2019.08143 |
| Makale Linki | http://www.techscience.com/cmc/v61n1/23099 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| Özet |
| 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 prelearned 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 … |
| Anahtar Kelimeler |
| Gradient Boosted Machines | Multi-Layer Perceptron | News popularity | Random Forest | Sentiment scores | Social network services |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 13 |
| Google Scholar | 15 |