Exploring the Impact of Machine Translation on Fake News Detection: A Case Study on Persian Tweets about COVID-19

Masood Hamed Saghayan, Seyedeh Fatemeh Ebrahimi, mohammad Bahrani

Abstract:

Fake news detection has become an emerging and critical topic of research in recent years. One of the major complications of fake news detection lies in the fact that news in social networks is multilingual, and therefore developing methods for each and every language in the world is impossible, especially for low resource languages like Persian. In an effort to solve this problem, researchers use machine translation to uniform the data and develop a method for the uniformed data. In this paper, we aim to explore the impacts of machine
translation on fake news detection. For this purpose, we extracted and labeled a dataset of Persian Tweets from Twitter on the subject of COVID-19 and developed a method for detecting fake news on the extracted Tweets based on the SVM classifier, then we machine translated the data and applied our proposed method to it. Finally, the result for binary class (only fake and legitimate) fake news detection was 87%, and for multiclass (satire, misinformation, neutral and legitimate) fake news detection was 62%, and our findings demonstrate that machine translation has a 4% negative impact on binary classification accuracy and a 23% negative impact on multiclass classification.