A Context-Enhanced Model for Fake News Detection
Received: 7 October 2024 | Revised: 12 November 2024 | Accepted: 16 November 2024 | Online: 22 December 2024
Corresponding author: Majdi Beseiso
Abstract
News published on social networks has a notable impact on changing people's perceptions on various topics. However, all news available on social media may not be genuine and might come from unverified sources. The prevalence of fake news is an inevitable concern that needs to be addressed effectively. This study presents an ensemble algorithm to improve fake news detection tools. Long-Short-Term-Memory (LSTM) and an ensemble of LSTM and Convolutional Neural Networks (CNN) were used. The proposed model used bidirectional LSTM layers and CNN convolutional 2D layers with kernel sizes of 2, 3, and 4 for 2-gram, 3-gram, and 4-gram tokens. The results obtained show an accuracy of 96.7% and 97.3% on a fake news dataset using the LSTM model and CNN-LSTM model, respectively, significantly improved from the maximum accuracy of 94.88% reported in a previous study. Embedding layers yielded significant improvements when paired with extended word sequences and pre-trained embedding vectors. Diverse tokenization methods with and without pre-trained embedding layers were also considered. The ensemble model achieved a 10.03% improvement in predictive accuracy on the Liar dataset, compared to the 6.08% improvement reported in a previous study using the same dataset.
Keywords:
convolutional neural networks, fake news, word2vec embedding, natural language processing, long short-term memoryDownloads
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Copyright (c) 2024 Majdi Beseiso, Saleh Al-Zahrani
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