Hoax Detection of Covid-19 News on Social Media using Convolutional Neural Network (CNN) and Support Vector Machine (SVM)

Arvia Dwi  Cahyani; Andi Kholik  Ramdani

doi:10.21108/ijoict.v9i2.872

Arvia Dwi Cahyani
Andi Kholik Ramdani

https://doi.org/10.21108/ijoict.v9i2.872

Abstract views: 68 ,

pdf downloads: 49

Keywords: hoax, detection, CNN, SVM, N-Gram

Abstract

It is undeniable that nowadays news spreads very quickly on social media. The ease of getting news on social media has resulted in some users using and spreading news without knowing the authenticity of the news. Twitter (X) users play an important role in spreading news on social media. In early 2020, cases of Covid-19 started to occur in Indonesia and some people spread news about Covid-19 without knowing the real information. The news is increasingly spreading through Twitter media which is shared by irresponsible people. This research builds a system that can detect hoax news on social media. The stages in this study started from crawling data, data preprocessing, word embedding, data separation, modeling process, and model evaluation. The methods used are Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The dataset used is news of Covid-19 in X Social media. The experiment showt that the use of the N-Gram Unigram + Bigram + Trigram combination on CNN produces an accuracy value of 75.8%, meanwhile in the SVM modeling produces 77.9%. It can be concluded that SVM has better performance than CNN in detecting hoax news,

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