An Artificial intelligence Approach to Fake News Detection in the Context of the Morocco Earthquake
Imane Ennejjai,
Anass Ariss,
Jamal Mabrouki,
Yasser Fouad,
Abdulatif Alabdultif,
Rajasekhar Chaganti,
Karima Salah Eddine,
Asmaa Lamjid and
Soumia Ziti
Data and Metadata, 2024, vol. 3, .377
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.
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.377:id:1056294dm2024377
DOI: 10.56294/dm2024.377
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