Fake News Detection Using Optimized CNN and LSTM Techniques

  • Emmy Danny Ajik Federal University Dutsinma, Nigeria http://orcid.org/0009-0007-0205-4837
  • Georgina N Obunadike Federal University Dutsinma, Nigeria
  • Faith O Echobu Federal University Dutsinma, Nigeria
Keywords: Optimisation, HyperOpt Technique, Fake News, Long Short Term Memory, Convolutional Neural Network

Abstract

Concerns have been raised about the social consequences of fake news as it has spread rapidly on online platforms. It is critical to detect and mitigate the spread of fake news in order to maintain a healthy community conversation. There is a need to put more effort into the identification of fake news as more people use the internet, especially as more internet-enabled gadgets become more widely available and inexpensive. With the help of two Neural Network techniques: long-short-term memory (LSTM) and Convolutional Neural Network (CNN). This research proposes novel deep-learning methods for identifying fake news using two datasets. These methods were considered for this research because they had proven to be successful in earlier studies that had been looked at. Finding the best-performing optimal models is the goal of this study. HyperOpt Technique was used for Neural Network model. The performance of the optimized models was compared with the performance of the models without optimization. The results obtained showed that for both datasets, CNN and LSTM performed better when training the models with the optimal values with an average difference of 12.7% for Accuracy, 11.9% for Precision, 12.3% for Recall and 15.4% for F1-Score.

Downloads

Download data is not yet available.

Author Biographies

Emmy Danny Ajik, Federal University Dutsinma

I am affiliated to the department of Computer Science, Faculty of Computing

Georgina N Obunadike, Federal University Dutsinma

Department of Computer Science, Faculty of Computing. Currently an Associate Professor

Faith O Echobu, Federal University Dutsinma

Department of Information Technology, Faculty of Computing

References

J. Zhang, B. Dong and P. S. Yu, “FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network,” in 2020 IEEE 36th International Conference on Data Engineering(ICDE), Dallas, Texas, 2020.

Q. Yumeng, “Predicting Future Rumours,” Chinese Journal of Electronics Volume: 27, p. 514 – 520, 2018.

S. Deepak and C. Bhadrachalam, “Deep neural approach to Fake-News identification,” in International Conference on Computational Intelligence and Data Science (ICCIDS 2019),, 10.1016/j.procs.2020.03.276, 2019.

P. Kaur, “Hybrid Text Classification Method for Fake News Detection.,” International Journal of Engineering and Advanced Technology (IJEAT) , pp. 2388-2392, 2019.

K. Poddar, D. G. Amali and K. S. Umadevi, “Comparison of Various Machine Learning Models for Accurate Detection of Fake News,” Innovations in Power and Advanced Computing Technologies (i-PACT). doi:10.1109/i-pact44901.2019.8960044, 2019.

M. A. Belhakimi, D. Ahlem and S. Giordano, “Merging deep learning model for fake news detection.,” in International Conference on Advanced Electrical Engineering (ICAEE), 2019.

M. Krešňáková, Viera, Sarnovsky, Martin and P. Butka, “Deep learning methods for Fake News detection.,” 10.1109/CINTI-MACRo49179.2019.9105317., 2019.

A. M. P. Braşoveanu and R. Andonie, “Integrating machine learning techniques in semantic fake news detection,” Neural Processing Letters, vol. 52, no. 2., 2020.

T. C. Truong, Q. B. Diep, I. Zelinka and R. Senkerik, “Supervised classification methods for fake news identification,” in in Proceedings of the ICAISC, Zakopane, Poland, 2020.

Z. Khanam, B. N. Alwasel, H. Sirafi and M. Rashid, “Fake News Detection Using Machine Learning Approaches,” in IOP Conf. Ser.: Mater. Sci. Eng. 1099 012040, 2021.

Y. Yang, 30 May 2017. [Online]. Available: https://drive.google.com/open?id=0B3e3qZpPtccsMFo5bk9Ib3VCc2c. [Accessed 26 February 2022].

R. Patodi, 13 September 2019. [Online]. Available: https://drive.google.com/file/d/1er9NTLUA3qnRuyhfzuN0XUsolC4a-_1/view.

A. Muzakir, K. Adi, R. Kusumaningrum, “Advancements in Semantic Expansion Techniques for Short Text Classification and Hate Speech Detectio,” Ingénierie des Systèmes d’Information, Vol. 28, No. 3, June, pp. 545-556, 2023.

M. Feurer and F. Hutter, “Hyperparameter Optimization. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds). Automated Machine Learning.,” in The Springer Series on Challenges in Machine Learning., 2019.

M. Claesen and B. Moor, “Hyperparameter Search in Machine Learning,” arXiv:1502.02127, 2015.

H. H. Aghdam and E. J. Heravi, “Guide to convolutional neural networks: A practical application to traffic-sign detection and classification,” Cham, Switzerland: Springer, 2017.

U. Ependi, A.F. Rochim, A. Wibowo, “A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 3, pp. 345-361, 2023.

Y. Yang, L. Zheng, J. Zhang, Q. Cui, X. Zhang, Z. Li and P. S. Yu, “TI-CNN: Convolutional Neural Networks for Fake News Detection,” Corr, 2018.

A. Abdullah, M. Awan, M. Shehzad and M. Ashraf, “Fake news classification bimodal using convolutional neural network and long short-term memory,” Int. J. Emerg. Technol. Learn, vol. 11, pp. 209-212, 2020.

J. A. Nasir, O. S. Khan and I. Varlamis, “Fake news detection: A hybrid CNN-RNN based Deep Learning Approach,” International Journal of Information Management Data Insights, vol. 1, no. 1, 2021.

Published
2023-08-31
Abstract views: 1323 times
Download PDF: 1367 times
How to Cite
Ajik, E., Obunadike, G., & Echobu, F. (2023). Fake News Detection Using Optimized CNN and LSTM Techniques. Journal of Information Systems and Informatics, 5(3), 1044-1057. https://doi.org/10.51519/journalisi.v5i3.548