Detection of fake news in social media using CNN with grey wolf optimized BERT

Rajalakshmi  Viswanathan; Sharon Femi Paul Sunder  Nathaniel; Kala  Alwarsamy; Khanaghavalle   Rajendran

doi:10.4025/actascitechnol.v47i1.73210

Authors

Rajalakshmi Viswanathan Sri Venkateswara College of Engineering https://orcid.org/0000-0001-7920-680X
Sharon Femi Paul Sunder Nathaniel Sri Venkateswara College of Engineering https://orcid.org/0000-0002-2984-9521
Kala Alwarsamy Sri Venkateswara College of Engineering
Khanaghavalle Rajendran Sri Venkateswara College of Engineering

DOI:

https://doi.org/10.4025/actascitechnol.v47i1.73210

Keywords:

BERT; N-gram; skip gram; grey wolf optimization; convolutional neural network.

Abstract

Fake news refers to stories that are falsely represented as news, that has the potential to misinform and mislead readers and identifying such fake news is a significant challenge to combat its impact in society. Our proposed work detects fake news using a hybrid method for fake news detection that uses Bidirectional Encoder Representations from Transformers (BERT) framework for feature extraction, Convolutional Neural Networks (CNN) for classification and Grey Wolf optimization for optimizing the parameters of BERT. The raw data is preprocessed and transformed into a format that can be fed as an input to the BERT model for feature extraction. This pre-trained model is fine-tuned using Grey wolf optimization (GWO) and CNN is used to classify each news article as real or fake. The proposed BERT-GWO-CNN model outperforms the existing machine learning models like Random Forest, SVM and deep learning namely RNN and LSTM in terms of prediction accuracy.

Downloads

Download data is not yet available.

References

Balshetwar, S. V., Rs, A., & R, D. J. (2023). Fake news detection in social media based on sentiment analysis using classifier techniques. Multimedia Tools and Applications, 82(23), 35781–35811. https://doi.org/10.1007/s11042-023-14883-3

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Version 2). arXiv. https://doi.org/10.48550/ARXIV.1810.04805

Fazil, M., & Abulaish, M. (2018). A hybrid approach for detecting automated spammers in Twitter. IEEE Trans-actions on Information Forensics and Security, 13(11), 2707–2719. https://doi.org/10.1109/TIFS.2018.2825958

Femi, P. S., & Vaidyanathan, S. G. (2022). An efficient ensemble framework for outlier detection using bio-inspired algorithm. International Journal of Bio-Inspired Computation, 19(2), 67. https://doi.org/10.1504/IJBIC.2022.121239

Femi, P. S., Vaidyanathan, S. G., & Kala, A. (2021). Integrating fuzzy constraint with feature correlation for local outlier mining. S?dhan?, 46(3), 172. https://doi.org/10.1007/s12046-021-01688-z

Ghosh, S., & Shah, C. (2018). Towards automatic fake news classification. Proceedings of the Association for Information Science and Technology, 55(1), 805–807. https://doi.org/10.1002/pra2.2018.14505501125

Goldani, M. H., Safabakhsh, R., & Momtazi, S. (2021). Convolutional neural network with margin loss for fake news detection. Information Processing & Management, 58(1), 102418. https://doi.org/10.1016/j.ipm.2020.102418

Jehad, R., & A.Yousif, S. (2020). Fake News Classification Using Random Forest and Decision Tree (J48). Al-Nahrain Journal of Science, 23(4), 49–55. https://doi.org/10.22401/ANJS.23.4.09

Kala, A., & Vaidyanathan, S. G. (2022). Forecasting monthly rainfall using bio-inspired artificial algae deep learning network. Fluctuation and Noise Letters, 21(02), 2250018. https://doi.org/10.1142/S0219477522500183

Kaliyar, R. K., Goswami, A., & Narang, P. (2021). EchoFakeD: Improving fake news detection in social media with an efficient deep neural network. Neural Computing and Applications, 33(14), 8597–8613. https://doi.org/10.1007/s00521-020-05611-1

Kaliyar, R. K., Goswami, A., Narang, P., & Sinha, S. (2020). FNDNet – A deep convolutional neural network for fake news detection. Cognitive Systems Research, 61, 32–44. https://doi.org/10.1016/j.cogsys.2019.12.005

Kula, S., Kozik, R., & Chora?, M. (2022). Implementation of the BERT-derived architectures to tackle disinformation challenges. Neural Computing and Applications, 34(23), 20449–20461. https://doi.org/10.1007/s00521-021-06276-0

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Watts, D. J., & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998

Mahara, G. S., & Gangele, S. (2022). Fake news detection: A RNN-LSTM, Bi-LSTM based deep learning approach. In 2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) (pp. 01–06). https://doi.org/10.1109/ICDDS56399.2022.10037403

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Ozbay, F. A., & Alatas, B. (2021). Adaptive Salp swarm optimization algorithms with inertia weights for novel fake news detection model in online social media. Multimedia Tools and Applications, 80(26–27), 34333–34357. https://doi.org/10.1007/s11042-021-11006-8

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

Rajalakshmi, V., & Ganesh Vaidyanathan, S. (2022a). Hybrid CNN-LSTM for Traffic Flow Forecasting. In G. Mathur, M. Bundele, M. Lalwani, & M. Paprzycki (Eds.), Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications (pp. 407–414). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-6332-1_35

Rajalakshmi, V., & Ganesh Vaidyanathan, S. (2022b). MLP-PSO framework with dynamic network tuning for traffic flow forecasting. Intelligent Automation & Soft Computing, 33(3), 1335–1348. https://doi.org/10.32604/iasc.2022.024310

Ruchansky, N., Seo, S., & Liu, Y. (2017). CSI: A Hybrid Deep Model for Fake News Detection. https://doi.org/10.48550/ARXIV.1703.06959

Saleh, H., Alharbi, A., & Alsamhi, S. H. (2021). OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection. IEEE Access, 9, 129471–129489. https://doi.org/10.1109/ACCESS.2021.3112806

Sastrawan, I. K., Bayupati, I. P. A., & Arsa, D. M. S. (2022). Detection of fake news using deep learning CNN–RNN based methods. ICT Express, 8(3), 396–408. https://doi.org/10.1016/j.icte.2021.10.003

Shu, K., Cui, L., Wang, S., Lee, D., & Liu, H. (2019). dEFEND: Explainable Fake News Detection. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 395–405. https://doi.org/10.1145/3292500.3330935

Smith, L. N. (2018). A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay (Version 2). arXiv. https://doi.org/10.48550/ARXIV.1803.09820

Sudhakar, M., & Kaliyamurthie K. P. (2023). Effective prediction of fake news using a learning vector quantization with hamming distance measure. Measurement: Sensors, 25, 100601. https://doi.org/10.1016/j.measen.2022.100601

Yang, Y., Zheng, L., Zhang, J., Cui, Q., Li, Z., & Yu, P. S. (2018). TI-CNN: Convolutional Neural Networks for Fake News Detection (Version 3). arXiv. https://doi.org/10.48550/ARXIV.1806.00749

Zhang, C., Gupta, A., Kauten, C., Deokar, A. V., & Qin, X. (2019). Detecting fake news for reducing misinformation risks using analytics approaches. European Journal of Operational Research, 279(3), 1036–1052. https://doi.org/10.1016/j.ejor.2019.06.022