Abstract
The pandemic we witnessed starting from December 2019, was accompanied by a significant rise in internet usage, and the social media, in particular, people were asked to stay at home to limit the spreading of the Covid-19 virus, this isolation made fake news a dangerous weapon that can directly harm people’s wellbeing and encourage antagonism and racism. Considering the re-al danger of this misinformation and disinformation, fake news research witnessed a surge of contributions that apply machine learning models, deep learning, and sentiment analysis, but among these models and especially those that use sentiment analysis, we found that there is a lack of the integration of the fuzzy aspect of our language, which may give more details and accuracy to the detection of fake news. In this work, we extend the classification model from our previous work by combining a deep learning algorithm LSTM with fuzzy logic for sentiment-aware classification of fake news. We experiment with a dataset that contains over than 13 K of covid-19 text content al-ready labeled as being real or fake, and we compared the results of our model with the state-of-the-art methods that do not incorporate fuzzy logic and sentiment for fake news classification. and we observed that our approach yields better results.
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Mohamed, B., Haytam, H., Abdelhadi, F. (2022). Applying Fuzzy Logic and Neural Network in Sentiment Analysis for Fake News Detection: Case of Covid-19. In: Lahby, M., Pathan, AS.K., Maleh, Y., Yafooz, W.M.S. (eds) Combating Fake News with Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-90087-8_19
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