An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation

Abstract

The misleading information during the coronavirus disease 2019 (COVID-19) pandemic’s peak time is very sensitive and harmful in our community. Analyzing and detecting COVID-19 information on social media are a crucial task. Early detection of COVID-19 information is very helpful and minimizes the risk of psychological security which leads to inconvenience in daily life. In this paper, a deep ensemble transfer learning framework with an understanding of the context of Arabic text COVID-19 information is proposed. This framework is inspired to spontaneously analyze and recognize the text about COVID-19. The ArCOVID-19Vac dataset has been used to train and test our proposed model. A comprehensive experimental study for each scenario is performed. For the binary classification scenario, the proposed framework records better evaluation results with 83.0%, 84.0%, 83.0%, and 84.0% in terms of accuracy, precision, recall, and F1-score, respectively. For the second scenario (three classes), the overall performance is recorded with an accuracy of 82.0%, precision of 80.0%, recall of 82.0%, and F1-score of 80.0%, respectively. In the last scenario with ten classes, the best evaluation performance results are recorded with an accuracy of 67.0%, a precision of 58.0%, a recall of 67.0%, and F1-score of 59.0%, respectively. In addition, we have applied an ensemble transfer learning model for this scenario to get 64.0%, 66.0%, 66.0%, and 65.0% in terms of accuracy, precision, recall, and F1-score, respectively. The results show that the proposed model through transfer learning provides better results for Arabic text than all state-of-the-art methods.