Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering

DOI: http://dx.doi.org/10.15439/2023R27

Citation: Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering, Pradeep Kumar, Manuel Cardona, Vijender Kumar Solanki, Tran Duc Tan, Abdul Wahid (eds). ACSIS, Vol. 38, pages 57–61 (2023)

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Abstract. Fake news has emerged as a significant challenge in today's information-driven society, where misinformation can spread rapidly and have detrimental consequences. Detecting and combatting fake news is crucial in maintaining the integrity of news sources and ensuring the public's access to accurate and reliable information. Machine learning approaches have recently demonstrated the ability to recognize false news stories automatically based on their features and content. To identify fake news, this study compares and contrasts several machine learning (ML) methods, including Random Forest, Passive Aggressive Classifier (PAC), Multinomial Naive Bayes, SVC, Decision tree, Gradient boosting, XG Boost, and Logistic Regression. These algorithms are tested on WELFake\_Dataset and the output received has shown a significant increase the accuracy and a decrease in the false rate.

References

1. Narra, M., Umer, M., Sadiq, S., Eshmawi, A. A., Karamti, H., Mohamed, A., & Ashraf, I. (2022). Selective feature sets based fake news detection for covid-19 to manage Infodemic. IEEE Access, 10, 98724–98736. https://doi.org/10.1109/access.2022.3206963
2. T. Smith, P. Husbands and M. O'Shea, "Neutral networks in an evolutionary robotics search space," Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Seoul, Korea (South), 2001, pp. 136-143 vol. 1, http://dx.doi.org/10.1109/CEC.2001.934382.
3. Seddari, Noureddine & Derhab, Abdelouahid & Belaoued, Mohamed & Halboob, Waleed & Al-Muhtadi, Jalal & Bouras, Abdelghani. (2022). A Hybrid Linguistic and Knowledge-Based Analysis Approach for Fake News Detection on Social Media. IEEE Access. 1-1. 10.1109/ACCESS.2022.3181184.
4. Rohera, Dhiren & Shethna, Harshal & Patel, Keyur & Thakker, Urvish & Tanwar, Sudeep & Gupta, Rajesh & Hong, Wei-Chiang & Sharma, Ravi. (2022). A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects. IEEE Access. 10. 10.1109/ACCESS.2022.3159651.
5. M. K. Elhadad, K. F. Li and F. Gebali, "Detecting Misleading Information on COVID-19," in IEEE Access, vol. 8, pp. 165201-165215, 2020, http://dx.doi.org/10.1109/ACCESS.2020.3022867.
6. H. Saleh, A. Alharbi and S. H. Alsamhi, "OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection," in IEEE Access, vol. 9, pp. 129471-129489, 2021, http://dx.doi.org/10.1109/ACCESS.2021.3112806.
7. Shahbazi, Zeinab & Byun, Yungcheol. (2021). Fake Media Detection Based on Natural Language Processing and Blockchain Approaches. IEEE Access. PP. 1-1. 10.1109/ACCESS.2021.3112607.
8. Rohera, Dhiren & Shethna, Harshal & Patel, Keyur & Thakker, Urvish & Tanwar, Sudeep & Gupta, Rajesh & Hong, Wei-Chiang & Sharma, Ravi. (2022). A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects. IEEE Access. 10. 10.1109/ACCESS.2022.3159651.
9. Stahl, K. (2018, April 20). Fake news detection in social media. California State University Stanislaus. Retrieved May 15, 2018.
10. Sahu, Mickey, and Narendra Sharma. "Research on the Ability to Detect Fake News with Machine Learning." International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 4, 2020, pp. 3664-3668., http://dx.doi.org/10.18531/ijert.2020.9.4.1590.
11. Xu, Y., Zhang, Y., Li, J., & Zhang, J. (2021). MVAN: Multi-view attention networks for fake news detection on social media. IEEE Access, 9, 106907-106917. http://dx.doi.org/10.1109/ACCESS.2021.3100245.
12. Balshetwar, S.V., RS, A. & R, DJ Fake news detection in social media based on sentiment analysis using classifier techniques. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-14883-3
13. Ali, H., Rehman, S. U., Shah, M. A., & Imran, M. (2021). All your fake detector are belong to us: Evaluating adversarial robustness of fake-news detectors under black-box settings. IEEE Access, 9, 81678-81692. http://dx.doi.org/10.1109/ACCESS.2021.3085875
14. M. K. Elhadad, K. F. Li and F. Gebali, "Detecting Misleading Information on COVID-19," in IEEE Access, vol. 8, pp. 165201-165215, 2020, http://dx.doi.org/10.1109/ACCESS.2020.3022867.
15. M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G. S. Choi and B. -W. On, "Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)," in IEEE Access, vol. 8, pp. 156695-156706, 2020, http://dx.doi.org/10.1109/ACCESS.2020.3019735.
16. Khan, S., Hakak, S., Deepa, N., Prabadevi, B., Dev, K., & Trelova, S. (2022). Detecting covid-19-related fake news using feature extraction. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.788074
17. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). Wiley-Interscience.
18. https://en.wikipedia.org/wiki/Support_vector_machine
19. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
20. Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2019). Fake News Detection on Social Media: A Data Mining Perspective. ACM Computing Surveys, 52(5), 1-34