Linguistic feature fusion for Arabic fake news detection and named entity recognition using reinforcement learning and swarm optimization

المؤلفون

  • Abdelghani Dahou School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China Faculty of Computer Sciences and Mathematics, Ahmed Draia University, Adrar 01000, Algeria المؤلف
  • Abd Elaziz Abd Elaziz Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt Faculty of Computer Science and Engineering, Galala University, Suez, Egypt Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon MEU Research Unit, Middle East University, Amman, 11831, Jordan المؤلف
  • Haibaoui Mohamed Faculty of Computer Sciences and Mathematics, Ahmed Draia University, Adrar 01000, Algeria المؤلف
  • Abdelhalim Hafedh Dahou GESIS – Leibniz-Institute for the Social Sciences Cologne, Germany المؤلف
  • Mohammed A.A. Al-qaness الجامعة الإماراتية الدولية image/svg+xml المؤلف
  • Mohamed Ghetas Faculty of Computer Science and Engineering, Galala University, Suez, Egypt المؤلف
  • Ahmed Ewess Department of Computer, Damietta University, Damietta, Egypt المؤلف
  • Zhonglong Zheng School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China Corresponding author at: School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China. المؤلف

DOI:

https://doi.org/10.1016/j.neucom.2024.128078

الكلمات المفتاحية:

Arabic fake news detection ، Deep learning ، Linguistic feature fusion ، Named entity recognition ، Reinforcement learning ، Swarm optimization

الملخص

In the context of the escalating use of social media in Arabic-speaking countries, driven by improved internet access, affordable smartphones, and a growing digital connectivity trend, this study addresses a significant challenge: the widespread dissemination of fake news. The ease and rapidity of spreading information on social media, coupled with a lack of stringent fact-checking measures, exacerbate the issue of misinformation. Our study examines how language features, especially Named Entity Recognition (NER) features, play a role in detecting fake news. We built two models: an AraBERT Multi-task Learning (MTL) based one for classifying Arabic fake news, and a token classification model that focuses on fake news NER features. The study combines embedding vectors from these models using an embedding fusion technique and applies machine learning algorithms for fake news detection in Arabic. We also introduced a feature selection algorithm named RLTTAO based on improving the Triangulation Topology Aggregation Optimizer (TTAO) performance using Reinforcement Learning and random opposition-based learning to enhance the performance by selecting relevant features, thereby improving the fusion process. Our results show that incorporating NER features enhances the accuracy of fake news detection in 5 out of 7 datasets, with an average improvement of 1.62%.

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التنزيلات

منشور

2024-07-12

إصدار

القسم

Articles

الفئات

كيفية الاقتباس

Dahou, A., Abd Elaziz, A. E., Mohamed, H., Dahou, A. H., Al-qaness, M. A., Ghetas, M., Ewess, A., & Zheng, Z. (2024). Linguistic feature fusion for Arabic fake news detection and named entity recognition using reinforcement learning and swarm optimization. المستودع الرقمي الجامعة الإماراتية الدولية, 1(1). https://doi.org/10.1016/j.neucom.2024.128078

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