A Deep Learning Based Algorithm Design for Fake News Detection Framework

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M. MadhuBala, Ajay Kumar Yadav, G. Sucharitha, P. Praveen Kumar


With the proliferation of social media, news is spreading faster. Moreover, social feedback became essential for organizations and governments to grow in intelligence and make strategies. However, there are cases of fake news that cause potential issues as they have wherewithal to influence people. The news in social media are polluted with fake news items if they are not identified correctly and removed from time to time. It is a challenging problem to be addressed. Many existing algorithms to detect fake news were good in performance. However, there is need for utilizing advanced Artificial Intelligence in the form of deep learning to leverage the state of the art. Towards this end, in this paper, an algorithm is designed to realize a fake news detection framework. The algorithm is known as Deep CNN based Fake New Detection (DCNN-FND) which exploits deep Convolutional Neural Network (CNN) and novel pre-processing mechanism with Natural Language Processing (NLP). The proposed framework implemented using Python data science platform, evaluated for its performance and compared with many existing techniques. The results revealed that the DCNN-FND shows better performance over the state of the art.

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How to Cite
P. Praveen Kumar, M. M. A. K. Y. G. S. . (2021). A Deep Learning Based Algorithm Design for Fake News Detection Framework. Annals of the Romanian Society for Cell Biology, 25(6), 4182–4192. Retrieved from https://annalsofrscb.ro/index.php/journal/article/view/6212