Rohit Kumar Kaliyar
ABSTRACT
During the pandemic of COVID-19, the propagation of fake news is spreading like wildfire on social media. Such fake news articles have created confusion among people and serious social disruptions as well. To detect such news articles effectively, we propose a generalized classification model (MCNNet) having the power of learning across different kernel-sized convolutional layers in different parallel channel network. The capability of MCNNet is lucrative towards any real-world fake news dataset. Experimental results have demonstrated the performance of our model with different real-world fake news datasets.
- Limeng Cui and Dongwon Lee. 2020. CoAID: COVID-19 Healthcare Misinformation Dataset. arXiv preprint arXiv:2006.00885(2020).Google Scholar
- Jiawei Zhang, Bowen Dong, and S Yu Philip. 2020. Fakedetector: Effective fake news detection with deep diffusive neural network. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1826–1829.Google ScholarCross Ref
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