Abstract
This paper aims to combat the Infodemic related to COVID-19. We propose an ensemble deep learning system for detecting misleading information related to COVID-19. This system depends on the shared COVID-19-related information from the official websites and Twitter accounts of the WHO, UNICEF, and UN, as well as the COVID-19 pre-checked facts from different fact-checking websites, as a source of reliable information to train the detection model. We use these collected data to build an ensemble system that uses several deep learning techniques to detect misleading information. To improve the performance of the proposed ensemble detection system, we implement a data preparation and preprocessing step, along with a features engineering step. We deploy Word Embedding based on a pre-trained word embedding list in addition to the existing word impeding in the input layer of the employed techniques. The experimental results are examined using fourteen performance measures (Accuracy, Error Rate, Loss, Precision, Recall, F1-Score, Area Under the Curve, Geometric-Mean, Specificity, Miss Rate, Fall-Out Rate, False-Discovery Rate, False-Omission Rate, and the Total Training Time). The obtained results are promising and indicate the quality and validity of the trusted information collected, for building misleading-information detection systems. It is worth noting that, in this paper, we use the terms “misleading information”, “misinformation”, and “fake news” interchangeably.
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Elhadad, M.K., Li, K.F., Gebali, F. (2021). An Ensemble Deep Learning Technique to Detect COVID-19 Misleading Information. In: Barolli, L., Li, K., Enokido, T., Takizawa, M. (eds) Advances in Networked-Based Information Systems. NBiS 2020. Advances in Intelligent Systems and Computing, vol 1264. Springer, Cham. https://doi.org/10.1007/978-3-030-57811-4_16
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DOI: https://doi.org/10.1007/978-3-030-57811-4_16
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