Language-Independent Features for Detecting Fake News: A Case Study of COVID-19 Twitter News Feed

W. K. Wong, Jeffery T.H. Kong, Filbert H. Juwono, Regina Reine

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


Social media platforms allow users to create and share information instantly. Due to the large amount of information on the social media platforms, some users may not be able to identify fake news. Fake news may be in a form of misinformation or disinformation and can lead to the loss of public trust on certain issues. It is crucial for social media users to detect the presence of fake news efficiently and in a timely manner. This paper aims to investigate the effectiveness of Support Vector Machine (SVM) classification approach to detect Twitter fake news. It is shown that SVM with linear kernel results in good accuracy of around 84% on independent test dataset. Furthermore, linear kernel function outperforms non-linear and polynomial kernel functions.

Original languageEnglish
Pages (from-to)12-17
Number of pages6
JournalIET Conference Proceedings
Issue number11
Publication statusPublished - 2023
Event2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023 - Hybrid, Iskandar Puteri, Malaysia
Duration: 10 Jul 202312 Jul 2023


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