The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
"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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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
Fake News Detection on Social Media: A Data Mining Perspective
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
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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