Comparing Traditional Machine Learning Methods for COVID-19 Fake News

被引：0

作者：

Almatarneh, Sattam ^{[1
]}

Gamallo, Pablo ^{[2
]}

Al Shargabi, Bassam ^{[3
]}

Al-Khassawneh, Yazan ^{[1
]}

Alzubi, Raed ^{[4
]}

机构：

[1] Zarqa Univ, Fac Informat Technol, Alzarqa, Jordan

[2] Univ Santiago Compostela, CiTIUS, Galiza, Spain

[3] Middle East Univ, Fac Informat Technol, Amman, Jordan

[4] King Faisal Univ, Dept Comp Sci, Al Hufuf, Saudi Arabia

来源：

2021 22ND INTERNATIONAL ARAB CONFERENCE ON INFORMATION TECHNOLOGY (ACIT) | 2021年

关键词：

Fake News; COVID-19; Supervised Machine Learning; Natural Language Processing;

D O I：

10.1109/ACIT53391.2021.9677453

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article describes some supervised classification techniques for COVID-19 fake news detection in English, where the sources of data are annotated posts from various social media platforms such as Twitter, Facebook, or Instagram. The main objective is to examine the performance of traditional machine learning techniques of COVID-19 fake news detection. In this situation, models trained with Support Vector Machine and Naive Bayes algorithms outperformed all other strategies.

引用

页码：736 / 739

页数：4

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