Citation

Tan, Kian Long and Lee, Chin Poo and Lim, Kian Ming (2021) Fake News Detection with Hybrid CNN-LSTM. In: 2021 9th International Conference on Information and Communication Technology (ICoICT), 3-5 Aug. 2021, Yogyakarta, Indonesia.

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Abstract

In the past decades, information and communication technology has developed rapidly. Therefore, social media has become the main platform for people to share and spread information to others. Although social media has brought a lot of convenience to people, fake news also spread more rapidly than before. This situation has brought a destructive impact to people. In view of this, we propose a hybrid model of Convolutional Neural Network and Long Short-Term Memory for fake news detection. The Convolutional Neural Network model plays the role of extracting representative high-level sequence features whereas the Long Short-Term Memory model encodes the long-term dependencies of the sequence features. Two regularization techniques are applied to reduce the model complexity and to mitigate the overfitting problem. The empirical results demonstrate that the proposed Convolutional Neural Network -Long Short-Term Memory model yields the highest F1-score on four fake news datasets.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Fake news, fake news detection, machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2021 07:07
Last Modified:	04 Nov 2021 07:07
URII:	http://shdl.mmu.edu.my/id/eprint/9765

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