Classification of fake news using multi-layer perceptron
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Maltepe Üniversitesi
Erişim Hakkı
CC0 1.0 Universal
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Özet
t. "Fake News (FNs) is defined as a made-up story to deceive or to mislead." The problem of FNs spread widely in recent years, especially on social media such as Facebook, Twitter, and other sources like webs and blogs. It has become a significant problem in society as a result of changing people’s ideas and opinions about the direction of this news. In this paper, FNs detection can be proposed by using the Term Frequency-Inverse Document Frequency (TF-IDF) as features extraction, and Multi-Layer perceptron (MLP) algorithm as a classifier. Two phases (feed-forward and back-propagation) are used with a three-layers, which are (input layer, one hidden layer, and output layer). After running our proposed algorithm on a FNs dataset, the classification accuracy achieved equals 95.47%.
Açıklama
Anahtar Kelimeler
Multi Layer Perceptron, NLP, TF-IDF, Fake news detection, text classification, News articles dataset
Kaynak
Fourth International Conference of Mathematical Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Jehad, R. ve Yousif, S. A. (2021). Classification of fake news using multi-layer perceptron. Fourth International Conference of Mathematical Sciences, Maltepe Üniversitesi. s. 1-5.
