Abstract
The misleading information brought by fake news has troubled our society for a long time. Recently, the increasing spreading rate of fake news has more severe consequences than ever in the past. Many types of neural networks have been applied to solve fake news detection and other natural language problems during these years. Nevertheless, due to the limitation of each structure, a hybrid neural network would often achieve a preferable accuracy. In this paper, a novel deep neural network is proposed for the fake news detection problem based on Convolutional Neural Network and Bi-directional Long Short Term Memory network. Sequential information will be captured by using Bi-LSTM and hidden features will be captured at a detailed level using CNN. The model will be tested on large-scale datasets, which demonstrated better performance than conventional neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Granik, M., Mesyura, V.: Fake new detection using naïve Bayes classifier. In: 2017 IEEE Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900–903. IEEE, Kyiv, Ukraine (2017)
Ahmad, I., Yousaf, M., Yousaf, S., Ahmad, M.O..: Fake news detection using machine learning ensemble methods. Complexity in Deep Neural Networks 1(1), (2020)
Yang, Y., Zheng, L., Zhang, J., Cui, Q., Li, Z., Yu, P.S.: TI-CNN: convolutional neural networks for fake news detection. arXiv (2018)
Dong, X., Victor, U., Qian, L.: Two-path deep semisupervised learning for timely fake news detection. IEEE Trans. Comput. Soc. Syst. 7(6), 1386–1398 (2020)
Aldwairi, M., Alwahedi, A.: Detecting fake news in social media networks. Procedia Comput. Sci. 141, 215–222 (2018)
Shen, L., Zhang, J.: Empirical evaluation of RNN architectures on sentence classification task. arXiv (2019)
Okoro, E.M., Abara, B.A., Umagba, A.O., Ajonye, A.A. Isa, Z.S.: A hybrid approach to fake news detection on social media. Nigerian Journal of Technology 37(2), 454–462 (2018)
Zhang, J., Dong, B, Philip, S.Y.: FAKEDETECTOR: effective fake news detection with deep diffusive neural network. arXiv (2018)
Zhang, X., Chen, F., Huang, R.: A combination of RNN and CNN for attention-based relation classification. Procedia Comput. Sci. 131(1), 911–917 (2018)
Bahad, P., Saxena, P., Kamal, R.: Fake news detection using bi-directional LSTM recurrent neural network. Procedia Comput. Sci. 165, 74–82 (2019)
Xu, T., Du, Y., Fu, C., Xie, C.: Incorporating forward and backward instances in a bi-lstm-cnn model for relation classification. In: 2018 IEEE 4th International Conference on Computer and Communications, pp. 2133–2137. IEEE, Chengdu, China (2018)
Colah Homepage. https://colah.github.io/posts/2015-08-Understanding-LSTMs. Accessed 24 Mar 2021
WILDML Homepage. http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/. Accessed 24 Mar 2021
Kaggle Homepage. https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset. Accessed 30 Dec 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ji, Y. (2021). Fake News Detection Based on a Bi-directional LSTM with CNN. In: Cao, W., Ozcan, A., Xie, H., Guan, B. (eds) Computing and Data Science. CONF-CDS 2021. Communications in Computer and Information Science, vol 1513. Springer, Singapore. https://doi.org/10.1007/978-981-16-8885-0_4
Download citation
DOI: https://doi.org/10.1007/978-981-16-8885-0_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8884-3
Online ISBN: 978-981-16-8885-0
eBook Packages: Computer ScienceComputer Science (R0)