Misinformation Detection in Arabic Tweets: A Case Study about COVID-19 Vaccination

Abstract

Misinformation about COVID-19 overwhelmed our lives due to the tremendous usage of social media, especially Twitter. Spreading misinformation caused fear and panic among people affecting the national economic security of many countries. Vaccination is the crucial key to limiting the pandemic spread of COVID-19. Therefore, researchers start to detect and fight against the spread of misinformation taking it as a new challenge. This paper illustrates a model for misinformation detection in Arabic tweets using Natural Language Processing (NLP) techniques. A machine learning-based system has been developed regarding COVID-19 vaccination tweets. Term Frequency-Inverse Document Frequency (TF-IDF) has been used as vector space model for feature extraction. Support Vector Machines classification algorithm has been used for implementation the proposed system. Evaluation of the system, using different metrics, has been implemented on Arcov-19Vac, a dataset of Arabic tweets related to COVID-19 vaccination. The results reported by the illustrated model show that the performance of our model is promising.