A Comparative Study of Machine Learning Approaches for Rumors Detection in Covid-19 Tweets

ABSTRACT

Analysing social media content becomes a crucial task due to the tremendous usage of social media platforms. In the era of COVID-19, detecting rumors becomes a vital task. In natural language processing, detecting rumors is a challenging task due to the complexity of rumors and tracking the source of rumors. In this paper, we proposed a machine learning-based model for rumors detection in COVID-19 related tweets for both English and Arabic Languages. Different machine learning algorithms have been implemented and Term Frequency/Inverse Document Frequency tf/idf has been used for feature extraction. The performance of all implemented classifiers has been analysed and compared. Our approach does not use external resources or data and depends only on the given training data. © 2022 IEEE.