Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory

Authors

  • Winda Kurnia Sari Universitas Sriwijaya
  • Iman Saladin B. Azhar Universitas Sriwijaya
  • Zaqqi Yamani Universitas Sriwijaya
  • Yesinta Florensia Universitas Sriwijaya

Keywords:

Fake News, CNN-BiLSTM, Word Embedding

Abstract

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.

Published

2024-10-01

Issue

Section

Articles