Abstract
Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.
Highlights
A novel coronavirus (COVID-19) was discovered in Wuhan, China, at the beginning of December 2019. e World Health Organization (WHO) has announced that the COVID-19 outbreak is a global pandemic on 11 March 2020 [1]
We have proposed an optimized hybrid model to detect the fake news on COVID-19 on social media. e core idea of the proposed model is the hybridization of using convolutional neural network (CNN) and long short-term memory (LSTM)
(ii) e proposed model is optimized using a Hyperopt optimization technique to select the optimal values of parameters (iii) e proposed model, CNN, LSTM, and regular ML algorithms are applied to three COVID-19 fake news datasets (iv) e experimental results demonstrated that the proposed model had achieved the best performance compared with other models e rest of this paper is structured as follows
Summary
A novel coronavirus (COVID-19) was discovered in Wuhan, China, at the beginning of December 2019. e World Health Organization (WHO) has announced that the COVID-19 outbreak is a global pandemic on 11 March 2020 [1]. Due to the panic from COVID-19 disease, people started posting fake news and misinformation about the coronavirus on social media networks. The researchers began to pay attention to COVID-19 misleading information by analyzing social media contents and applying advanced AI technologies (i.e., machine learning and deep learning) to profiling the COVID-19 fake news [4]. As a result of the research direction in content analysis, the research organizations start raising funding to provide novel solutions to combat COVID-19 in terms of analyzing the misleading information about the COVID-19 pandemic [5,6,7,8,9,10]. We have proposed an optimized hybrid model to detect the fake news on COVID-19 on social media.
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