A Hybrid Method Based on CRITIC Method and Machine Learning Models for Effective Fake News Detection in Thai Language


  • Mongkol Saensuk Department of Mathematics and Statistics, Faculty of Science and Information Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon 47000, Thailand
  • Suwiwat Witchakool Department of Mathematics and Statistics, Faculty of Science and Information Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon 47000, Thailand
  • Atchara Choompol Department of Computer Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand




CRITIC method, Decision Tree, ensemble machine learning, fake news detection, K-Nearest Neighbors, Naive Bayes


Fake news has emerged as a pervasive issue within the modern information ecosystem, leading to widespread dissemination of misinformation and erosion of trust. This paper introduces a novel hybrid approach for effectively detecting fake news in the Thai language by combining the CRITIC method with multiple machine learning models. The initial step involves collecting Thai-language fake news data from websites. Subsequently, the data undergoes a preprocessing phase. In the second step, the preprocessed data is used for validation through three basic machine learning models, namely, Naive Bayes, Decision Tree, and K-Nearest Neighbors. In the third step, the accuracy results from these three models are employed to calculate the significance weights of each model using the CRITIC method. In the final step, predictions are recalculated using the proposed method. The proposed method achieves an 83.37% accuracy, surpassing Naive Bayes (80.83%), Decision Tree (80.37%), and K-Nearest Neighbors (75.75%). This indicates a significant enhancement in performance, with the proposed method outperforming the established models by up to 7.62%.  Consequently, the proposed method can enhance the performance of fake news detection in Thai language by utilizing an ensemble of the original models. A significant advantage of this approach is its simplicity coupled with high efficacy. It is postulated that this method can be adapted for detecting fake news in other languages as well.


Ahmad, U., Khan, A., & Saeid, A. B. (2023). Integrated multi-criteria group decision-making methods based on q-rung picture fuzzy sets for the identification of occupational hazards. Soft Computing, 2023, 1–24. https://doi.org/10.1007/s00500-023-08154-4

Al-shami, T. M., & Mhemdi, A. (2023). Generalized frame for orthopair fuzzy sets:(m, n)-fuzzy sets and their applications to multi-criteria decision-making methods. Information, 14(1), Article 56. https://doi.org/10.3390/info14010056

Alghamdi, J., Lin, Y., & Luo, S. (2022). A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection. Information, 13, Article 576. https://doi.org/10.3390/info13120576

Alghamdi, J., Lin, Y., & Luo, S. (2023). Does Context Matter? Effective Deep Learning Approaches to Curb Fake News Dissemination on Social Media. Applied Sciences, 13(5), Article 3345.

Alinezhad, A., & Khalili, J. (2019). New Methods and Applications in Multiple Attribute Decision Making (MADM). Springer. 10.1007/978-3-030-15009-9

Allcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211

Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1

Chumnankit, T. & Siriborvornratanakul, T. (2022). Thai Fake News Detection Using Natural Language Processing. KMUTT Research & Development Journal, 45(2), 275-287. https://doi.org/10.14456/kmuttrd.2022.16

Gururaj, H. L., Lakshmi, H., Soundarya, B. C., Flammini, F., & Janhavi, V. (2022). Machine Learning-Based Approach for Fake News Detection. Journal of ICT Standardization, 10(4), 509–530. https://doi.org/10.13052/jicts 2245-800X.1042

Jlifi, B., Sakrani, C., & Duvallet, C. (2022). Towards a soft three-level voting model (Soft T-LVM) for fake news detection. Journal of Intelligent Information Systems, 61(1), 249-269. https://doi.org/10.1007/s10844-022-00769-7

Khanmohammadi, S., & Rezaei, N. (2021). Role of Toll‐like receptors in the pathogenesis of COVID‐19. Journal of Medical Virology, 93(5), 2735–2739. https://doi.org/10.1002/ jmv.26826

Kongwan, A., Kamaruddin, S. S., & Ahmad, F. K. (2022). Anaphora resolution in Thai EDU segmentation. Journal of Computer Science, 18, 306-315. https://doi.org/10.3844/jcssp.2022.306.315

Lai, Y.-J., Liu, T.-Y., & Hwang, C.-L. (1994). Topsis for MODM. European Journal of Operational Research, 76(3), 486–500. https://doi.org/10.1016/0377-2217(94) 90282-8

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., … Rothschild, D. (2018). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998

Liu, Y., & Wu, Y.-F. (2018). Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Proceedings of the AAAI conference on artificial intelligence 32(1). https://doi.org/10.1609/aaai.v32i1. 11268

Murugesan, S., & Pachamuthu, K. (2022). Fake News Detection in the Medical Field Using Machine Learning Techniques. International Journal of Safety & Security Engineering, 12(6), 723-727. https://doi.org/10.18280/ ijsse.120608

Nasawat, P., Talangkun, S., Arunyanart, S., & Wichapa, N. (2021). Hybrid cross-efficiency approach based on ideal and anti-ideal points and the critic method for ranking decision-making units: a case study on ranking the methods of rice weevil disinfestation. Decision Science Letters, 10(3), 375–392.

Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1

Pandey, S., Prabhakaran, P., Reddy, N. V. S., & Acharya, D. (2022). Fake News Detection from Online media using Machine Learning Classifiers. Journal of Physics: Conference Series. 2161, 1-12. https://doi.org/10.1088/ 1742-6596/2161/1/012027

Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36. https://doi.org/10.1145/3137597.3137600

Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: methods and applications. CRC press.

Wang, H., Dou, Y., Chen, C., Sun, L., Yu, P. S., & Shu, K. (2023). Attacking Fake News Detectors via Manipulating News Social Engagement. ArXiv Preprint ArXiv:2302.07363. https://doi.org/10.48550/ arXiv.2302.07363

Wang, T.-C., & Lee, H.-D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, 36(5), 8980–8985. https://doi.org/10.1016/j.eswa.2008.11.035

Wichapa, N., Khokhajaikiat, P., & Chaiphet, K. (2021). Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste. Decision Science Letters, 10(1), 79–92. https://doi.org/10.5267/j.dsl.2020.10.001

Xu, L., & Yang, J.-B. (2001). Introduction to multi-criteria decision making and the evidential reasoning approach (Vol. 106). Manchester School of Management Manchester. https://personalpages.manchester.ac.uk/staff/jian-bo.yang/JB%20Yang%20Book_ Chapters/XuYang_MSM_WorkingPaperFinal.pdf

Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019). Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). ArXiv Preprint ArXiv:1903. 08983. https://doi.org/10.18653/v1/S19-2010

Zeleny, M. (2012). Multiple criteria decision making Kyoto 1975 (Vol. 123). Springer Science & Business Media.

Zhou, X., & Zafarani, R. (2018). Fake news: A survey of research, detection methods, and opportunities. ArXiv Preprint ArXiv:1812.00315, 2. https://doi.org/10. 48550/arXiv.1812.00315




How to Cite

Saensuk, M., Witchakool, S., & Choompol, A. (2024). A Hybrid Method Based on CRITIC Method and Machine Learning Models for Effective Fake News Detection in Thai Language. Journal of Current Science and Technology, 14(2). https://doi.org/10.59796/jcst.V14N2.2024.24



Research Article