The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A Stylometric Inquiry into Hyperpartisan and Fake News
Open accessAbstract
We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news.A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97% of the 299 fake news articles identified are also hyperpartisan.We show how a style analysis can distinguish hyperpartisan news from the mainstream (F 1 = 0.78), and satire from both (F 1 = 0.81).But stylometry is no silver bullet as style-based fake news detection does not work (F 1 = 0.46).We further reveal that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream.This result is robust: it has been confirmed by three different modeling approaches, one of which employs Unmasking in a novel way.Applications of our results include partisanship detection and pre-screening for semi-automatic fake news detection.
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"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Wang, W. Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2067 · Cited by 1,516
The General Inquirer: A Computer Approach to Content Analysis
Green, B. F., Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M. (1967) · American Educational Research Journal · doi:10.2307/1161774 · Cited by 1,013
Open information extraction from the web
Etzioni, O., Banko, M., Soderland, S., & Weld, D. S. (2008) · Communications of the ACM · doi:10.1145/1409360.1409378 · Cited by 1,012
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1317 · Cited by 870
Prominent Features of Rumor Propagation in Online Social Media
Kwon, S., Cha, M., Jung, K., Chen, W., & Wang, Y. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.61 · Cited by 859
Limiting the spread of misinformation in social networks
Budak, C., Agrawal, D., & El Abbadi, A. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963499 · Cited by 807
Horne, B., & Adali, S. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14976 · Cited by 688
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
Ma, J., Gao, W., & Wong, K.-F. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1066 · Cited by 637
Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News
Rubin, V., Conroy, N., Chen, Y., & Cornwell, S. (2016) · Proceedings of the Second Workshop on Computational Approaches to Deception Detection · doi:10.18653/v1/w16-0802 · Cited by 584
Automatic Detection of Fake News
Pérez‐Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.07104 · Cited by 502
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
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
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
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
Metadata
- Authors
- Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B.
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 231-240
- OpenAlex
- W2593408211
- Cited by
- 686
- References
- 33
- FWCI
- 236.16
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B. (2018). A Stylometric Inquiry into Hyperpartisan and Fake News. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p18-1022
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