E Almandouh Mohammed, Alrahmawy Mohammed F, Eisa Mohamed, Elhoseny Mohamed, Tolba A S
Portsaid University, Portsaid, Egypt.
Mansoura University, Mansoura, Egypt.
Sci Rep. 2024 Nov 4;14(1):26591. doi: 10.1038/s41598-024-76286-0.
Social media has emerged as a dominant platform where individuals freely share opinions and communicate globally. Its role in disseminating news worldwide is significant due to its easy accessibility. However, the increase in the use of these platforms presents severe risks for potentially misleading people. Our research aims to investigate different techniques within machine learning, deep learning, and ensemble learning frameworks in Arabic fake news detection. We integrated FastText word embeddings with various machine learning and deep learning methods. We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. The research methodology involves utilizing two Arabic news article datasets, AFND and ARABICFAKETWEETS datasets, categorized into fake and real subsets and applying comprehensive preprocessing techniques to the text data. Four hybrid deep learning models are presented: CNN-LSTM, RNN-CNN, RNN-LSTM, and Bi-GRU-Bi-LSTM. The Bi-GRU-Bi-LSTM model demonstrated superior performance regarding the F1 score, accuracy, and loss metrics. The precision, recall, F1 score, and accuracy of the hybrid Bi-GRU-Bi-LSTM model on the AFND Dataset are 0.97, 0.97, 0.98, and 0.98, and on the ARABICFAKETWEETS dataset are 0.98, 0.98, 0.99, and 0.99 respectively. The study's primary conclusion is that when spotting fake news in Arabic, the Bi-GRU-Bi-LSTM model outperforms other models by a significant margin. It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.
社交媒体已成为一个主导平台，个人可在该平台上自由分享观点并进行全球交流。由于其易于访问，它在全球传播新闻方面的作用重大。然而，这些平台使用的增加给可能误导人们带来了严重风险。我们的研究旨在调查机器学习、深度学习和集成学习框架内用于检测阿拉伯语假新闻的不同技术。我们将FastText词嵌入与各种机器学习和深度学习方法相结合。然后，我们利用了基于Transformer的先进模型，包括BERT、XLNet和RoBERTa，并通过仔细调整超参数来优化其性能。研究方法包括使用两个阿拉伯语新闻文章数据集，即AFND和ARABICFAKETWEETS数据集，这些数据集被分类为假新闻和真实新闻子集，并对文本数据应用全面的预处理技术。我们提出了四种混合深度学习模型：CNN-LSTM、RNN-CNN、RNN-LSTM和Bi-GRU-Bi-LSTM。Bi-GRU-Bi-LSTM模型在F1分数、准确率和损失指标方面表现出卓越性能。混合Bi-GRU-Bi-LSTM模型在AFND数据集上的精确率、召回率、F1分数和准确率分别为0.97、0.97、0.98和0.98，在ARABICFAKETWEETS数据集上分别为0.98、0.98、0.99和0.99。该研究的主要结论是，在识别阿拉伯语假新闻时，Bi-GRU-Bi-LSTM模型明显优于其他模型。它为未来将假新闻检测扩展到多种语言的研究奠定了基础，极大地助力了全球打击虚假信息的斗争。
