The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
Open accessAbstract
We present an analytic study on the language of news media in the context of political fact-checking and fake news detection.We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text.To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale.Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Taming Black Swans: Challenges for Sustainable Strategic Crisis Management: Plenary Session
Pelletier, P. (2021) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.1111/j.1468 · Cited by 6,460
Lord, C. G., Ross, L., & Lepper, M. R. (1979) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.37.11.2098 · Cited by 4,583
Natural language processing with Python
Bird, S., Klein, E., & Loper, E. (2009) · CERN Document Server (European Organization for Nuclear Research) · Cited by 3,468
Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05 · doi:10.3115/1220575.1220619 · Cited by 3,345
Character-level Convolutional Networks for Text Classification
Zhang, X., Zhao, J., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.01626 · Cited by 3,205
Linguistic Inquiry and Word Count (LIWC): Pronounced “Luke,” . . . and Other Useful Facts
Chung, C. K., & Pennebaker, J. W. (2012) · Applied Natural Language Processing · doi:10.4018/978-1-60960-741-8.ch012 · Cited by 2,151
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
"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
Cited by
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
Self-citationCombating 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
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
Lu, Y.-J., & Li, C.-T. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.48 · Cited by 427
Fake news detection on social media using geometric deep learning
Monti, F., Frasca, F., Eynard, D., Mannion, D., & Bronstein, M. M. (2019) · arXiv · doi:10.48550/arxiv.1902.06673 · Cited by 392
Metadata
- Authors
- Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y.
- Venue
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Published
- 2017
- Pages
- 2931-2937
- OpenAlex
- W2759820691
- Cited by
- 870
- References
- 26
- FWCI
- 220.97
- Type
- conference paper
- Language
- en
- Open access
- hybrid
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking. https://app.cochat.ai/papers/10.18653/v1/d17-1317
- The paper
- Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y. (2017). Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d17-1317
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
