Fact Check Using Multinomial Naive Bayes
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-1502-3_81
Reference9 articles.
1. Bedi, A., Pandey, N., Khatri, S.K.: A framework to identify and secure the issues of fake news and rumours in social networking. In: 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC), Greater Noida, India, pp. 70–73. https://doi.org/10.1109/PEEIC47157.2019.8976800
2. Pradhan, M.A., Shinde, A., Dhiman, R., Ghorpade, S., Jawale, S.: Fake news detection methods: machine learning approach. Int. J. Res. Appl. Sci. Eng. Technol. 8(VII) (IJRASET), 971–975. ISSN: 2321-9653. https://doi.org/10.22214/ijraset.2020.29630
3. Gilda, S.: Notice of violation of IEEE publication principles: evaluating machine learning algorithms for fake news detection. In: 2017 IEEE 15th Student Conference on Research and Development (SCOReD), Putrajaya, 2017, pp. 110–115, https://doi.org/10.1109/SCORED.2017.8305411
4. Kim, S.-B., Han, K.-S., Rim, H.-C., Hyon Myaeng, S.: Some effective techniques for Naive Bayes text classification. IEEE Trans. Knowl. Data Eng. 18(11), 1457–1466 (2006). https://doi.org/10.1109/TKDE.2006.180
5. Conroy, N.J., Rubin, V.L., Chen, Y.: Automatic deception detection: methods for finding fake news. Proc. Assoc. Inf. Sci. Technol. 52(1), 14 (2015)
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