Papers › An Enhanced Fake News Detection System With Fuzzy Deep Learning
An Enhanced Fake News Detection System With Fuzzy Deep Learning
Cheng Xu, M-Tahar Kechadi
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.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fact Checking | LIAR2 | FDHN | Accuracy (Test) | 0.702 | #1 of 1 | Archive leaderboard | report |
| Fact Checking | LIAR2 | FDHN | F1-Macro (Test) | 0.696 | #1 of 1 | Archive leaderboard | report |
| Fact Checking | LIAR2 | FDHN | F1-Micro (Test) | 0.702 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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