An Enhanced Fake News Detection System With Fuzzy Deep Learning

Abstract

Addressing the intricate challenge of fake news detection, traditionally reliant on the expertise of professional fact-checkers due to the inherent uncertainty in fact-checking processes, this research leverages advancements in language models to propose a novel fuzzy logic-based network. The proposed model is specifically tailored to navigate the uncertainty inherent in the fake news detection task. The evaluation is conducted on the well-established LIAR dataset, a prominent benchmark for fake news detection research, yielding state-of-the-art results. Moreover, recognizing the limitations of the LIAR dataset, we introduce LIAR2 as a new benchmark, incorporating valuable insights from the academic community. Our study presents detailed comparisons and ablation experiments on both LIAR and LIAR2 datasets and establishes our results as the baseline for LIAR2. The proposed approach aims to enhance our understanding of dataset characteristics, contributing to refining and improving fake news detection methodologies.

Keywords

• Deep learning
• fuzzy deep learning
• fake news
• fake news detection
• fact-checking
• NLP