Fake news Detection Using Naive Bayes Classifier


  • Rahul Srivastava Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Lucknow Campus, India
  • Dr. Pawan Singh Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Lucknow Campus, India https://orcid.org/0000-0002-1342-9493




Artificial Intelligence, Fuzzy Logic, Fuzzy Inference, Machine Learning, Naive Based Classifier, Prediction, Recommendation, Support Vector Machine


Fake news has been on the rise thanks to rapid digitalization across all platforms and mediums. Many governments throughout the world are attempting to address this is-sue. The use of Natural Language Processing and Machine Learning techniques to properly identify fake news is the subject of this research. The data is cleaned, and fea-ture extraction is performed using pre-processing techniques. Then, employing four dis-tinct strategies, a false news detection model is created. Finally, the research examines and contrasts the accuracy of Naive Bayes, Support Vector Machine (SVM), neural net-work, and long short-term memory (LSTM) methodologies in order to determine which is the most accurate. To clean the data and conduct feature extraction, pre-processing technologies are needed. Then, employing four distinct strategies, a false news detec-tion model is created. Finally, in order to determine the best fit for the model, the re-search explores and analyzes the accuracy of Naive Bayes, Support Vector Machine (SVM), neural network, and long short-term memory (LSTM) approaches. The proposed model is working well with an accuracy of products up to 93.6%.


Download data is not yet available.


M. Granik and V. Mesyura, “Fake news detection using naive Bayes classifier,” 2017 IEEE 1st Ukr. Conf. Electr. Comput. Eng. UKRCON 2017 - Proc., pp. 900–903, 2017.

A. Martínez-Garcia, S. Morris, M. Tscholl, F.Tracy, and P. Carmichael, “Case-based learning, pedagogical innovation, and semantic web technologies,” IEEE Trans. Learn. Technol., vol. 5, no. 2, pp. 104–116, 2012.

P. R. Humanante-Ramos, F. J. Garcia-Penalvo, and M. A. Conde-Gonzalez, “PLEs in Mobile Contexts: New Ways to Personalize Learning,” Rev. Iberoam. Tecnol. del Aprendiz., vol. 11, no. 4, pp. 220–226, 2016.

T. Granskogen and J. A. Gulla, “Fake news detection: Network data from social media used to predict fakes,” CE Workshop Proc., vol. 2041, no. 1, pp. 59–66, 2017.

R. V. L, C. Yimin, and C. N. J, “Deception detection for news: Three types of fakes,”Proc. Assoc. Inf. Sci. Technol., vol. 52, no. 1, pp. 1–4, 2016.

V. Rubin, N. Conroy, Y. Chen, and S. Cornwell, “Fake news or truth? Using satirical cues to detect potentially misleading news,” in Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pp. 7–17, 2016.

Z. Jin, J. Cao, Y. Zhang, J. Zhou, and Q. Tian, “Novel Visual and Statistical Image Features for Microblogs News Verification,” IEEE Trans. Multimed., vol. 19, no. 3, pp. 598–608, 2017.

S. Gilda, "Notice of Violation of IEEE Publication Principles: Evaluating machine learning algorithms for fake news detection," 2017 IEEE 15th Student Conference on Research and Development (SCOReD), 2017, pp. 110-115, doi: 10.1109/SCORED.2017.8305411.

Y. Seo, D. Seo and C. -S. Jeong, "FaNDeR: Fake News Detection Model Using Media Reliability," TENCON 2018 - 2018 IEEE Region 10 Conference, 2018, pp. 1834-1838, doi: 10.1109/TENCON.2018.8650350.

S. Das Bhattacharjee, A. Talukder and B. V. Balantrapu, "Active learning-based news veracity detection with feature weighting and deep-shallow fusion," 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 556-565, doi: 10.1109/BigData.2017.8257971.

S. Helmstetter and H. Paulheim, “Weakly supervised learning for fake news detection on Twitter,” Proc. 2018 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2018, pp. 274–277, 2018.

S. B. Parikh, V. Patil, and P. K. Atrey, “On the Origin, Proliferation, and Tone of Fake News,” Proc. - 2nd Int. Conf. Multimed. Inf. Process. Retrieval, MIPR 2019, pp. 135–140, 2019.

A. Dey, R. Z. Rafi, S. Hasan Parash, S. K. Arko, and A. Chakrabarty, “Fake news pattern recognition using linguistic analysis, 2018 Jt. 7th Int. Conf. Informatics, Electron. Vis. 2nd Int. Conf. Imaging, Vis. Pattern Recognition, ICIEV-IVPR 2018, pp. 305–309, 2019.

N. Kim, D. Seo and C. Jeong, "FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information," 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), 2018, pp. 617-620, doi: 10.1109/ICSESS.2018.8663864.

R. L. Vander Wal, V. Bryg, and M. D. Hays, “X-Ray Photoelectron Spectroscopy (XPS) Applied to Soot & What It Can Do for You,” Notes, pp. 1–35, 2006.

M. Gahirwal, “Fake News Detection,” International Journal of Advance Research, Ideas, and Innovations in Technology, vol. 4, no. 1, pp. 817–819, 2018.

M. L. Della Vedova, E. Tacchini, S. Moret, G. Ballarin, M. DiPierro and L. de Alfaro, "Automatic Online Fake News Detection Combining Content and Social Signals," 2018 22nd Conference of Open Innovations Association (FRUCT), 2018, pp. 272-279, doi: 10.23919/FRUCT.2018.8468301.

J. Kapusta, P. Hájek, M. Munk, and Ľ. Benko, “Comparison of fake and real news based on morphological analysis,” Procedia Comput. Sci., vol. 171, pp. 2285–2293, 2020.




How to Cite

R. Srivastava and P. Singh, “Fake news Detection Using Naive Bayes Classifier”, J. Manage. Serv. Sci., vol. 2, no. 1, pp. 1–7, Feb. 2022.




Case Study

Most read articles by the same author(s)

1 2 > >>