Papers › Fuzzy Deep Hybrid Network for Fake News Detection
Fuzzy Deep Hybrid Network for Fake News Detection
Cheng Xu, M-Tahar Kechadi
The proliferation of fake news in the digital age poses a significant threat to the democratic process and undermines trust in the media. As disinformation campaigns become more sophisticated and pervasive, it has become increasingly challenging to discern credible news sources from deceptive ones. Machine learning and deep learning techniques have shown promise in automatically detecting fake news, but there is still room for improvement. In this paper, we propose an innovative fuzzy logic-based hybrid model to improve the performance of fake news detection. The model leverages a combination of news articles and textual and numerical context information. We evaluate our proposed model on a fact-checking benchmark dataset and achieve state-of-the-art results. Our findings suggest that combining fuzzy logic with deep learning can improve fake news detection and provide a reliable tool for combatting disinformation. The code is available at https://github.com/chengxuphd/FDHN
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