Author:
Ennejjai Imane,Ariss Anass,Mabrouki Jamal,Fouad Yasser,Alabdultif Abdulatif,Chaganti Rajasekhar,Salah Eddine Karima,Lamjid Asmaa,Ziti Soumia
Abstract
The catastrophic earthquake that struck Morocco on Septem- ber 8, 2023, garnered significant media coverage, leading to the swift dissemination of information across various social media and online plat- forms. However, the heightened visibility also gave rise to a surge in fake news, presenting formidable challenges to the efficient distribution of ac- curate information crucial for effective crisis management. This paper introduces an innovative approach to detection by integrating Natural language processing, bidirectional long-term memory (Bi-LSTM), con- volutional neural network (CNN), and hierarchical attention network (HAN) models within the context of this seismic event. Leveraging ad- vanced machine learning,deep learning, and data analysis techniques, we have devised a sophisticated fake news detection model capable of precisely identifying and categorizing misleading information. The amal- gamation of these models enhances the accuracy and efficiency of our system, addressing the pressing need for reliable information amidst the chaos of a crisis.
Publisher
Salud, Ciencia y Tecnologia
Reference107 articles.
1. 1. AKHTAR, Pervaiz, et al. Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. Annals of Operations Research, 2023, 327.2: 633-657.
2. 2. A¨IMEUR, Esma; AMRI, Sabrine; BRASSARD, Gilles. Fake news, disinforma-
3. tion and misinformation in social media: a review. Social Network Analysis and Mining, 2023, 13.1: 30.
4. 3. CAPUANO, Nicola, et al. Content Based Fake News Detection with machine and
5. deep learning: a systematic review. Neurocomputing, 2023.
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