A systemic literature overview of Fake News Challenge (FNC-1) dataset and its use in fake news detection schemes
*Zainab A. JawadCorresponding authorZainaba.alhasani@student.uokufa.edu.iqDepartment of Computer ScienceFaculty of Computer Science and MathematicsUniversity of KufaNajaf, IraqView full profile → , Ahmed J. Obaidahmedj.aljanaby@uokufa.edu.iqAffiliation 1Department of Computer ScienceFaculty of Computer Science and MathematicsUniversity of KufaNajaf, IraqAffiliation 2National University of Science and TechnologyThi-Qar, IraqView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 02 Mar 2022
- Accepted:
- 02 Apr 2022
- Published Online:
- 15 Jul 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-1567
- Pages:
- 1197–1206
Abstract
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References
[1] Shu, K., et. al., Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 2017. 19(1): p. 22-36.
[2] Rubin, V.L., On deception and deception detection: Content analysis of computer-mediated stated beliefs. Proceedings of the American society for information science and technology, 2010. 47(1): p. 1-10.
[3] Pogue, D., How to Stamp Out Fake News. Scientific American, 2017. 316(2): p. 24-24.
[4] Conroy, N.K., V.L. Rubin, and Y. Chen, Automatic deception detection: Methods for finding fake news. Proceedings of the association for information science and technology, 2015. 52(1): p. 1-4.
[5] Asr, F. and M. Taboada, Misinfotext: a collection of news articles, with false and true labels. 2019.
[6] Zhou, X. and R. Zafarani, A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR), 2020. 53(5): p. 1-40.
[7] Fake News Challenge Stage 1 (FNC-I): Stance Detection. Available from: http://www.fakenewschallenge.org.
[8] Riedel, B., et. al., A simple but tough-to-beat baseline for the Fake News Challenge stance detection task. arXiv preprint arXiv:1707.03264, 2017.
[9] Bhatt, G., et. al., On the benefit of combining neural, statistical and external features for fake news identification. arXiv preprint arXiv:1712.03935, 2017.
[10] Mohtarami, M., et. al., Automatic stance detection using end-to-end memory networks. arXiv preprint arXiv:1804.07581, 2018.
[11] Hanselowski, A., et. al., A retrospective analysis of the fake news challenge stance detection task. arXiv preprint arXiv:1806.05180, 2018.
[12] Borges, L., B. Martins, and P. Calado, Combining similarity features and deep representation learning for stance detection in the context of checking fake news. Journal of Data and Information Quality (JDIQ), 2019. 11(3): p. 1-26.
[13] Schiller, B., J. Daxenberger, and I. Gurevych, Stance detection benchmark: How robust is your stance detection? KI-Künstliche Intelligenz, 2021. 35(3): p. 329-341.
[14] Denaux, R. and J.M. Gomez-Perez. Linked credibility reviews for explainable misinformation detection in International Semantic Web Conference. 2020. Springer.
[15] Felber, T., Constraint 2021: Machine learning models for COVID-19 fake news detection shared task. arXiv preprint arXiv:2101.03717, 2021.
[16] Daniel, A.M., Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? arXiv preprint arXiv:2111.05139, 2021.
[17] Shubham Sharma & Ahmed J. Obaid (2020) Mathematical modelling, analysis and design of fuzzy logic controller for the control of ventilation systems using MATLAB fuzzy logic toolbox, Journal of Interdisciplinary Mathematics, 23:4, 843-849, DOI: 10.1080/09720502.2020.1727611.
