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"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Open accessAbstract
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 metadata with text.We show that this hybrid approach can improve a text-only deep learning model.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv · doi:10.48550/arxiv.1107.4557 · Cited by 1,008
Cited by
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Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1074 · Cited by 1,343
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00626-4 · Cited by 1,191
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184
A Survey of Deep Active Learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169
An overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997
Fake news, disinformation and misinformation in social media: a review
Aïmeur, E., Amri, S., & Brassard, G. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01028-5 · Cited by 772
A survey on fake news and rumour detection techniques
Bondielli, A., & Marcelloni, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.035 · Cited by 658
Defending Against Neural Fake News
Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1905.12616 · Cited by 569
Combating Fake News: A Survey on Identification and Mitigation Techniques
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3305260 · Cited by 514
A Survey on Automated Fact-Checking
Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00454 · Cited by 500
“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content
Molina, M. D., Sundar, S. S., Le, T., & Lee, D. (2021) · American Behavioral Scientist · doi:10.1177/0002764219878224 · Cited by 485
GLTR: Statistical Detection and Visualization of Generated Text
Gehrmann, S., Strobelt, H., & Rush, A. M. (2019) · arXiv · doi:10.48550/arxiv.1906.04043 · Cited by 460
GLTR: Statistical Detection and Visualization of Generated Text
Gehrmann, S., Strobelt, H., & Rush, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/p19-3019 · Cited by 470
“Other scenarios include the possibility of generating false articles (Wang, 2017) or misleading reviews (Fornaciari and Poe-sio, 2014).”
BackgroundFact or Fiction: Verifying Scientific Claims
Wadden, D., Lin, S., Lo, K., Wang, L. L., van Zuylen, M., Cohan, A., & Hajishirzi, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.609 · Cited by 382
False Information on Web and Social Media: A Survey
Kumar, S., & Shah, N. (2018) · arXiv · doi:10.48550/arxiv.1804.08559 · Cited by 289
TabFact: A Large-scale Dataset for Table-based Fact Verification
Chen, W., Wang, H., Chen, J., Zhang, Y., Wang, H., Li, S., Zhou, X., & Wang, W. Y. (2019) · arXiv · doi:10.48550/arxiv.1909.02164 · Cited by 239
Self-citationMedia-Rich Fake News Detection: A Survey
Parikh, S. B., & Atrey, P. K. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00093 · Cited by 237
Fake News Detection in Social Networks via Crowd Signals
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018) · Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 · doi:10.1145/3184558.3188722 · Cited by 229
Combating disinformation in a social media age
Shu, K., Bhattacharjee, A., Alatawi, F., Nazer, T. H., Ding, K., Karami, M., & Liu, H. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1385 · Cited by 198
Explainable Automated Fact-Checking for Public Health Claims
Kotonya, N., & Toni, F. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.623 · Cited by 195
Deep Learning Algorithms for Detecting Fake News in Online Text
Girgis, S., Amer, E., & Gadallah, M. (2018) · 2018 13th International Conference on Computer Engineering and Systems (ICCES) · doi:10.1109/icces.2018.8639198 · Cited by 186
Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review
Capuano, N., Fenza, G., Loia, V., & Nota, F. D. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.005 · Cited by 183
MetaICL: Learning to Learn In Context
Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169
Gongane, V. U., Munot, M. V., & Anuse, A. D. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00951-3 · Cited by 153
Reasoning Over Semantic-Level Graph for Fact Checking
Zhong, W., Xu, J., Tang, D., Xu, Z., Duan, N., Zhou, M., Wang, J., & Yin, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.549 · Cited by 151
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Yao, Y., Viswanath, B., Cryan, J., Zheng, H., & Zhao, B. Y. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133990 · Cited by 150
Feng, S., Park, C. Y., Liu, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.656 · Cited by 149
RARR: Researching and revising what language models say, using language models
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V., Lao, N., Lee, H., Juan, D.-C., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.910 · Cited by 141
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
Schuster, T., Fisch, A., & Barzilay, R. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.52 · Cited by 126
Jiang, S., & Wilson, C. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274351 · Cited by 125
The Future of False Information Detection on Social Media: New Perspectives and Trends
Guo, B., Ding, Y., Yao, L., Liang, Y., & Yu, Z. (2021) · ACM Computing Surveys · doi:10.1145/3393880 · Cited by 122
Fake News vs Satire: A Dataset and Analysis
Golbeck, J., Mauriello, M., Auxier, B., Bhanushali, K. H., Bonk, C., Bouzaghrane, M. A., Buntain, C., Chanduka, R., Cheakalos, P., Everett, J. B., et al. (2018) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3201064.3201100 · Cited by 121
The limitations of stylometry for detecting machine-generated fake news
Schuster, T., Schuster, R., Shah, D. J., & Barzilay, R. (2020) · Computational Linguistics · doi:10.1162/coli_a_00380 · Cited by 109
Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims
Chen, S., Khashabi, D., Yin, W., Callison-Burch, C., & Roth, D. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1053 · Cited by 104
ClaimsKG: A Knowledge Graph of Fact-Checked Claims
Tchechmedjiev, A., Fafalios, P., Boland, K., Gasquet, M., Zloch, M., Zapilko, B., Dietze, S., & Todorov, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-30796-7_20 · Cited by 92
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
Zannettou, S., Sirivianos, M., Blackburn, J., & Kourtellis, N. (2019) · Journal of Data and Information Quality · doi:10.1145/3309699 · Cited by 86
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Uchendu, A., Ma, Z., Le, T., Zhang, R., & Lee, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.172 · Cited by 82
Fake news or true lies? Reflections about problematic contents in marketing
Di Domenico, G., & Visentin, M. (2020) · International Journal of Market Research · doi:10.1177/1470785320934719 · Cited by 77
“Some authors suggest the need to provide tools to improve fact-checking, assuming that individuals might change their mind when confronted with the evidence of facts (Talwar et al., 2019; Wang, 2017).”
BackgroundEvidence-based Fact-Checking of Health-related Claims
Sarrouti, M., Ben Abacha, A., Mrabet, Y., & Demner-Fushman, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.297 · Cited by 71
True or False: Studying the Work Practices of Professional Fact-Checkers
Micallef, N., Armacost, V., Memon, N., & Patil, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512974 · Cited by 70
On Sentiment of Online Fake News
Zaeem, R. N., Li, C., & Barber, K. S. (2020) · 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381323 · Cited by 61
Giachanou, A., Ríssola, E. A., Ghanem, B., Crestani, F., & Rosso, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-51310-8_17 · Cited by 58
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., & Chen, W. (2023) · arXiv · doi:10.48550/arxiv.2305.11738 · Cited by 561
“Our method is also inspired by fact-checking in journalism (Wang, 2017) that assesses whether a claim made by a human is true (Thorne et al.”
MethodSoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
Singhal, M., Ling, C., Paudel, P., Thota, P., Kumarswamy, N., Stringhini, G., & Nilizadeh, S. (2023) · 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp57164.2023.00056 · Cited by 57
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Shao, R., Wu, T., Wu, J., Nie, L., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367749 · Cited by 53
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
Fung, Y., Thomas, C., Gangi Reddy, R., Polisetty, S., Ji, H., Chang, S.-F., McKeown, K., Bansal, M., & Sil, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.133 · Cited by 52
Detecting Fake News with Tweets’ Properties
Nyow, N. X., & Chua, H. N. (2019) · 2019 IEEE Conference on Application, Information and Network Security (AINS) · doi:10.1109/ains47559.2019.8968706 · Cited by 51
DialFact: A Benchmark for Fact-Checking in Dialogue
Gupta, P., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.263 · Cited by 48
A Review on Fact Extraction and Verification
Bekoulis, G., Papagiannopoulou, C., & Deligiannis, N. (2023) · ACM Computing Surveys · doi:10.1145/3485127 · Cited by 48
Fake News Types and Detection Models on Social Media A State-of-the-Art Survey
Collins, B., Hoang, D. T., Nguyen, N. T., & Hwang, D. (2020) · Communications in Computer and Information Science · doi:10.1007/978-981-15-3380-8_49 · Cited by 45
Generating fact checking briefs
Fan, A., Piktus, A., Petroni, F., Wenzek, G., Saeidi, M., Vlachos, A., Bordes, A., & Riedel, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.580 · Cited by 42
Dataset of Fake News Detection and Fact Verification: A Survey
Murayama, T. (2021) · arXiv · doi:10.48550/arxiv.2111.03299 · Cited by 40
BREAKING! Presenting Fake News Corpus for Automated Fact Checking
Pathak, A., & Srihari, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop · doi:10.18653/v1/p19-2050 · Cited by 39
Hallucination to truth: a review of fact-checking and factuality evaluation in large language models
Rahman, S. S., Islam, M. A., Alam, M. M., Zeba, M., Rahman, M. A., Chowa, S. S., Raiaan, M. A. K., & Azam, S. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11454-w · Cited by 39
Evidence-based Factual Error Correction
Thorne, J., & Vlachos, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.256 · Cited by 39
(2019) · doi:10.18653/v1/d19-1292 · Cited by 36
Generating Literal and Implied Subquestions to Fact-check Complex Claims
Chen, J., Sriram, A., Choi, E., & Durrett, G. (2022) · arXiv · doi:10.48550/arxiv.2205.06938 · Cited by 34
Generating Literal and Implied Subquestions to Fact-check Complex Claims
Chen, J., Sriram, A., Choi, E., & Durrett, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.229 · Cited by 34
User-Characteristic Enhanced Model for Fake News Detection in Social Media
Jiang, S., Chen, X., Zhang, L., Chen, S., & Liu, H. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32233-5_49 · Cited by 34
Building towards Automated Cyberbullying Detection: A Comparative Analysis
Al-Harigy, L. M., Al-Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4794227 · Cited by 32
The Effects of Crowd Worker Biases in Fact-Checking Tasks
Draws, T., La Barbera, D., Soprano, M., Roitero, K., Ceolin, D., Checco, A., & Mizzaro, S. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3534629 · Cited by 23
Benchmark Data Contamination of Large Language Models: A Survey
Xu, C., Guan, S., Greene, D., & Kechadi, M.-T. (2024) · arXiv · doi:10.48550/arxiv.2406.04244 · Cited by 22
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.06904 · Cited by 20
Srba, I., Razuvayevskaya, O., Leite, J. A., Moro, R., Schlicht, I. B., Tonelli, S., García, F. M., Lottmann, S. B., Teyssou, D., Porcellini, V., et al. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3770077 · Cited by 19
Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Akhtar, M., Cocarascu, O., & Simperl, E. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.30 · Cited by 18
How effectively can machines defend against machine-generated fake news? An empirical study
Bhat, M. M., & Parthasarathy, S. (2020) · Proceedings of the First Workshop on Insights from Negative Results in NLP · doi:10.18653/v1/2020.insights-1.7 · Cited by 16
Damacena Duarte, J., Cândido, G. D., De Britto Filho, J. R. A., Souza Neto, J., Da Costa, E. J., Da Costa, J. P. J., & Peotta De Melo, L. (2026) · IEEE Access · doi:10.1109/access.2026.3656849 · Cited by 15
Combining Human and Machine Confidence in Truthfulness Assessment
Qu, Y., Roitero, K., Barbera, D. L., Spina, D., Mizzaro, S., & Demartini, G. (2023) · Journal of Data and Information Quality · doi:10.1145/3546916 · Cited by 6
Eldifrawi, I., Wang, S., & Trabelsi, A. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.361 · Cited by 5
Metadata
- Authors
- Wang, W. Y.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2017
- Pages
- 422-426
- OpenAlex
- W2611921107
- Cited by
- 1,516
- References
- 3
- FWCI
- 378.31
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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