TY - GEN
T1 - Comparing traditional machine learning methods for covid-19 fake news
AU - Almatarneh, Sattam
AU - Gamallo, Pablo
AU - ALshargabi, Bassam
AU - AL-Khassawneh, Yazan
AU - Alzubi, Raed
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/12/23
Y1 - 2021/12/23
N2 - 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 Na¨ıve Bayes algorithms outperformed all other strategies.
AB - 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 Na¨ıve Bayes algorithms outperformed all other strategies.
KW - COVID-19
KW - Fake news
KW - Natural language processing
KW - Supervised machine learning
UR - https://www.scopus.com/pages/publications/85125338248
U2 - 10.1109/ACIT53391.2021.9677453
DO - 10.1109/ACIT53391.2021.9677453
M3 - Conference contribution
AN - SCOPUS:85125338248
T3 - 2021 22nd International Arab Conference on Information Technology, ACIT 2021
BT - 2021 22nd International Arab Conference on Information Technology, ACIT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Arab Conference on Information Technology, ACIT 2021
Y2 - 21 December 2021 through 23 December 2021
ER -