1. Abdelali, A., Darwish, K., Durrani, N., Mubarak, H.: Farasa: a fast and furious segmenter for Arabic. In: DeNero, J., Finlayson, M., Reddy, S. (eds.) Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 11–16. Association for Computational Linguistics, San Diego, California (2016)

   Google Scholar 

2. Agarwal, A., Mittal, M., Pathak, A., Goyal, L.M.: Fake news detection using a blend of neural networks: an application of deep learning. SN Comput. Sci.1, 1–9 (2020)

   Article MATH  Google Scholar 

3. Ahmed, H., Traore, I., Saad, S.: Detection of online fake news using n-gram analysis and machine learning techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) ISDDC 2017. LNCS, vol. 10618, pp. 127–138. Springer, Cham (2017).https://doi.org/10.1007/978-3-319-69155-8_9

   Chapter MATH  Google Scholar 

4. Geirhos, R., et al.: Shortcut learning in deep neural networks. Nat. Mach. Intell.2(11), 665–673 (2020)

   Article MATH  Google Scholar 

5. Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Trans. Assoc. Comput. Linguist.10, 178–206 (2022)

   Article MATH  Google Scholar 

6. Iyyer, M., Manjunatha, V., Boyd-Graber, J., Daumé III, H.: Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (volume 1: Long papers), pp. 1681–1691 (2015)

   Google Scholar 

7. Liu, C.Z., Sheng, Y.X., Wei, Z.Q., Yang, Y.Q.: Research of text classification based on improved tf-idf algorithm. In: 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), pp. 218–222. IEEE (2018)

   Google Scholar 

8. Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning–based text classification: a comprehensive review. ACM Comput. Surv.54(3) (2021)

   Google Scholar 

9. Qaiser, S., Ali, R.: Text mining: use of tf-idf to examine the relevance of words to documents. Int. J. Comput. Appl.181(1), 25–29 (2018)

   MATH  Google Scholar 

10. Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: Fakenewsnet: a data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprintarXiv:1809.01286 (2018)

11. Smaili, K., Hamza, A., Langlois, D., Amazouz, D.: Boutef: bolstering our understanding through an elaborated fake news corpus. In: International conference on Arabic language processing. Springer (2024)

    Google Scholar 

12. de Souza, M.C., Nogueira, B.M., Rossi, R.G., Marcacini, R.M., Dos Santos, B.N., Rezende, S.O.: A network-based positive and unlabeled learning approach for fake news detection. Mach. Learn.111(10), 3549–3592 (2022)

    Article MathSciNet MATH  Google Scholar 

13. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res.15(1), 1929–1958 (2014)

    MathSciNet MATH  Google Scholar 

14. Verma, P.K., Agrawal, P., Amorim, I., Prodan, R.: Welfake: word embedding over linguistic features for fake news detection. IEEE Trans. Comput. Soc. Syst.8(4), 881–893 (2021)

    Article  Google Scholar 

15. Vo, N., Lee, K.: Where are the facts? Searching for fact-checked information to alleviate the spread of fake news. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020) (2020)

    Google Scholar 

16. Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science359(6380), 1146–1151 (2018)

    Article MATH  Google Scholar 

17. Wang, W.Y.: “liar, liar pants on fire”: A new benchmark dataset for fake news detection. In: Barzilay, R., Kan, M.Y. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 422–426. Association for Computational Linguistics, Vancouver, Canada (2017)

    Google Scholar 

18. Wynne, H.E., Wint, Z.Z.: Content based fake news detection using n-gram models. In: Proceedings of the 21st International Conference on Information Integration and Web-Based Applications and Services, pp. 669–673 (2019)

    Google Scholar 

19. Zeng, X., Abumansour, A.S., Zubiaga, A.: Automated fact-checking: a survey. Lang. Linguist. Compass15(10), e12438 (2021)

    Article  Google Scholar 

20. Zhou, X., Zafarani, R.: Network-based fake news detection: a pattern-driven approach. SIGKDD Explor. Newsl.21(2), 48–60 (2019).https://doi.org/10.1145/3373464.3373473

Download references