LIAR: Fake News Detection Benchmark
A LIAR (fact-checking benchmark) dataset of 12,789 labeled claim statements from Politifact, collected by Wang via Politifact fact-checking (2017). Each record contains claim text, 6-class veracity label, speaker metadata (party, state, job title), credit history counts (barely_true/false/half_true/mostly_true/pants_fire), and context. The dataset is notable for its benchmark dataset for fake news detection with fine-grained 6-class labels, rich speaker metadata, and credit history. Intended for research into fake news detection, fact-checking, political discourse analysis, and misinformation research.
Cite this dataset at
liar.echo.directory
Analytics
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Computed over the full corpus.
Record
- Catalog key
liar- Authors
- Wang, William Yang
- Year
- 2017
- Source paper
- 'Liar, Liar Pants on Fire': A New Benchmark Dataset for Fake News Detection
- DOI / arXiv
- 10.18653/v1/P17-2067
- License
- Academic use only (see original sources) (LicenseRef-Research-Only)
- Records
- 12,789
- Text corpus
- claim text
- Languages
- en
- Collected
- 2017 (2010s)
- Tasks
- Fake news detection (multi-class;
label) inferred
Provenance
- Originally published at
- https://sites.cs.ucsb.edu/~william/software.html link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-09, 2.9 MB across 4 files. Checksums in MANIFEST.json.
Citations
“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection
William Yang Wang
Journal Article · Annual Meeting of the Association for Computational Linguistics · 2017
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- Tensor Factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks
Hang-Jun Che, Bai-Cheng Pan et al. · IEEE Transactions on Computational Social Systems · 2024 · cited 57× - QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims
Venktesh V, Abhijit Anand et al. · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2024 · cited 56× - Hallucination to truth: a review of fact-checking and factuality evaluation in large language models
Subhey Sadi Rahman, Md. Adnanul Islam et al. · Artificial Intelligence Review · 2025 · cited 53× - Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
Zhenrui Yue, Hui-Min Zeng et al. · Annual Meeting of the Association for Computational Linguistics · 2024 · cited 50× - Multimodal Misinformation Detection using Large Vision-Language Models
Sahar Tahmasebi, Eric Müller-Budack et al. · International Conference on Information and Knowledge Management · 2024 · cited 46×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@inproceedings{wang2017liar,
title = {{'Liar, Liar Pants on Fire': A New Benchmark Dataset for Fake News Detection}},
author = {Wang, William Yang},
booktitle = {ACL},
year = {2017},
doi = {10.18653/v1/P17-2067}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Wang, W. Y. (2017). LIAR: Fake News Detection Benchmark [Data set]. https://doi.org/10.18653/v1/P17-2067
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Wang, William Yang. LIAR: Fake News Detection Benchmark. 2017, doi.org/10.18653/v1/P17-2067.