Papers › "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
William Yang Wang
Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present liar: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.
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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 |
|---|---|---|---|---|---|---|---|
| Fake News Detection | LIAR | Hybrid CNNs (Text + All) | Test Accuracy | 0.274 | #1 of 4 | Archive leaderboard | report |
| Fake News Detection | LIAR | Hybrid CNNs (Text + All) | Validation Accuracy | 0.247 | #1 of 4 | Archive leaderboard | report |
| Fake News Detection | LIAR | CNNs | Test Accuracy | 0.27 | #2 of 4 | Archive leaderboard | report |
| Fake News Detection | LIAR | CNNs | Validation Accuracy | 0.26 | #2 of 4 | Archive leaderboard | report |
| Fake News Detection | LIAR | Hybrid CNNs (Text + Speaker) | Test Accuracy | 0.248 | #3 of 4 | Archive leaderboard | report |
| Fake News Detection | LIAR | Hybrid CNNs (Text + Speaker) | Validation Accuracy | 0.277 | #3 of 4 | Archive leaderboard | report |
| Fake News Detection | LIAR | Bi-LSTMs | Test Accuracy | 0.233 | #4 of 4 | Archive leaderboard | report |
| Fake News Detection | LIAR | Bi-LSTMs | Validation Accuracy | 0.223 | #4 of 4 | 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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