Deep Learning for Fake News Detection: Literature Review
DOI:
https://doi.org/10.23851/mjs.v34i2.1292Keywords:
Deep learning, fake news detection, natural language processing, machine learning, text classification, information credibility, social media analysis, semantic analysis, CNN, RNN, multimodal, datasets, word embedding, LSTM, Hybrid, BERTAbstract
The use of Deep Learning (DL) for identifying false or misleading information, known as fake news, is a growing area of research. Deep learning, a form of machine learning that utilizes algorithms to learn from large data sets, has shown promise in detecting fake news. The spread of fake news can cause significant harm to society economically, politically, and socially, and it has become increasingly important to find ways to detect and stop its spread. This paper examines current studies that use deep learning methods, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), as well as the multi-model approach, to detect fake news. It also looks at the use of word embedding models to convert text to vector representations and the datasets used for training models. Furthermore, the paper discusses the use of the attention mechanism in conjunction with deep learning to process sequential data.
Received: 26/01/2023
Revised: 06/03/2023
Accepted: 05/04/2023
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References
Jason Brownlee. 2017. Deep Learning for Natural Language Processing. Machine Learning Mastery.
Ahmed, H., Traore, I., Saad, S. (2017). Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore, I., Lounging, I., Awad, A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science (), vol 10618. Springer, Cham.
Goodfellow, I., Bengio, Y., & Courville, A. (2017). Deep learning (adaptive computation and machine learning series). Cambridge Massachusetts, 321-359.
Wang, William. (2017). "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. 422-426. 10.18653/v1/P17-2067.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., &Polosukhin, I. (2017). Attention Is All You Need. arXiv.
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-236.
Yamashita, R., Nishio, M., Do, R.K.G. et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611-629 (2018).
Wei, Bhardwaj, A., & Wei, J. (2018). Deep Learning Essentials (1st ed.). Packt Publishing. Retrieved from https://www.perlego.com/book/578845/deep-learning-essentials-pdf (Original work published 2018)
Yang, Y., Zheng, L., Zhang, J., Cui, Q., Li, Z., & Yu, P. S. (2018). TI-CNN: Convolutional Neural Networks for Fake News Detection. arXiv.
Bode, L., & Vraga, E. K. (2018). See something, say something: Correction of global health misinformation on social media. Health communication, 33(9), 1131-1140.
Moolayil, J. (2019). An Introduction to Deep Learning and Keras. In: Learn Keras for Deep Neural Networks. Apress, Berkeley, CA.
Dong, X., Victor, U., Chowdhury, S., & Qian, L. (2019). Deep Two-path Semi-supervised Learning for Fake News Detection. ArXiv, abs/1906.05659.
Kapoor, Amita&Guili, Antonio & Pal, Sujit. (2019). Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition.
S. Singhal, R. R. Shah, T. Chakraborty, P. Kumaraguru and S. Satoh, "SpotFake: A Multi-modal Framework for Fake News Detection," 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), 2019, pp. 39-47.
Wang, Y., Han, H., Ding, Y., Wang, X., Liao, Q. (2019). Learning Contextual Features with Multi-head Self-attention for Fake News Detection. In: Xu, R., Wang, J., Zhang, LJ. (eds) Cognitive Computing - ICCC 2019. ICCC 2019. Lecture Notes in Computer Science (), vol 11518. Springer, Cham.
Masciari, E., Moscato, V., Picariello, A., Sperli, G. (2020). A Deep Learning Approach to Fake News Detection. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science (), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_11
Kaliyar, Rohit & Goswami, Anurag & Narang, Pratik & Sinha, Soumendu. (2020). FNDNet- A Deep Convolutional Neural Network for Fake News Detection. Cognitive Systems Research. 61.
Hu, Q., Li, Q., Lu, Y. et al. Multi-level word features based on CNN for fake news detection in cultural communication. Pers UbiquitComput 24, 259-272 (2020).
M. D. P. P. Goonathilake and P. P. N. V. Kumara, "CNN, RNN-LSTM Based Hybrid Approach to Detect State-of-the-Art Stance-Based Fake News on Social Media," 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), 2020, pp. 23-28.
Zhou, X., Wu, J., &Zafarani, R. (2020). SAFE: Similarity-Aware Multi-Modal Fake News Detection. arXiv.
M. Qazi, M. U. S. Khan and M. Ali, "Detection of Fake News Using Transformer Model," 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2020, pp. 1-6.
Vajjala, S., Majumder, B., Gupta, A., & Surana, H. (2020). Practical natural language processing: a comprehensive guide to building real-world NLP systems. O'Reilly Media.
Albahar, Marwan. (2021). A hybrid model for fake news detection: Leveraging news content and user comments in fake news. IET Information Security. 15.
Petratos, Pythagoras. (2021). Misinformation, disinformation, and fake news: Cyber risks to business. Business Horizons. 64.
Mridha, M. F., Keya, A. J., Hamid, M. A., Monowar, M. M., & Rahman, M. S. (2021). A Comprehensive Review on Fake News Detection with Deep Learning. IEEE Access.
G, S.K. (2021). Deep Learning for Fake News Detection. In: Data Science for Fake News. The Information Retrieval Series, vol 42. Springer, Cham.
Chakraborty, T. (2021). Multi-modal Fake News Detection. In: Data Science for Fake News. The Information Retrieval Series, vol 42. Springer, Cham.
Ravichandiran, S. (2021). Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT. Packt Publishing Ltd.
G, S.K. (2021). Deep Learning for Fake News Detection. In: Data Science for Fake News. The Information Retrieval Series, vol 42. Springer, Cham.
Rothman, D. (2021). Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more. Packt Publishing Ltd.
Trueman, T.E., J, A., Narayanasamy, P., & Vidya, J. (2021). Attention-based C-BiLSTM for fake news detection. Appl. Soft Comput., 110, 107600.
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