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In an era where misinformation spreads rapidly, the need for reliable and interpretable fake news detection systems is critical. This study evaluates deep learning models—Fully Connected Neural Networks (FCNN), Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), Long Short-Term Memory (LSTM) networks, and FastText—for fake news classification. To ensure interpretability, Local Interpretable Model-agnostic Explanations (LIME) are applied to FCNN, CNN, and GCN, generating human-understandable explanations for predictions. While LSTM and FastText are included for performance comparison, they are excluded from interpretability analysis due to technical constraints. The models are trained using different input representations: TF-IDF for FCNN and CNN, and graph structures for GCN. This paper analyzes trade-offs between accuracy and explainability, offering insights into the effectiveness of deep learning models for fake news detection and contributing to the development of more transparent AI solutions.

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Interpretable Fake News Detection Using Neural Networks and LIME

  • 会议论文

  • 发表日期：

    2026年

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  • Mrunal Vibhute,
  • Anjali Naik

摘要

In an era where misinformation spreads rapidly, the need for reliable and interpretable fake news detection systems is critical. This study evaluates deep learning models—Fully Connected Neural Networks (FCNN), Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), Long Short-Term Memory (LSTM) networks, and FastText—for fake news classification. To ensure interpretability, Local Interpretable Model-agnostic Explanations (LIME) are applied to FCNN, CNN, and GCN, generating human-understandable explanations for predictions. While LSTM and FastText are included for performance comparison, they are excluded from interpretability analysis due to technical constraints. The models are trained using different input representations: TF-IDF for FCNN and CNN, and graph structures for GCN. This paper analyzes trade-offs between accuracy and explainability, offering insights into the effectiveness of deep learning models for fake news detection and contributing to the development of more transparent AI solutions.