Abstract
Fake news formerly defined as a form of news that consists of misinformation or hoaxes. It can spread through different channels such as print and broadcast media or online social media. News helps us to keep up with the latest trends and knowledge of the world. But when fake news is spread, it creates wrong impressions in the mind of people. A spread of fake news could be done to achieve anything ranging from political to monetary gain by manipulating the general public into thinking. Nowadays, the spread of fake news articles has become easier than ever due to easy access to the Internet and the high engagement rates of the general public which misleads them; this has become a problem of high concern, and to mitigate this problem, many types of research have been carried out in the recent times. This paper aims to mitigate the problem of fake news by using a computational model that can help to detect fake news. The model presented in this paper is a hybrid approach of URL detection, convolutional neural networks (CNN) and random forest (RF). Two CNN models have been merged with different metadata of news article body and title and a random forest classifier for authorship classification. After merging all the models, we got 95.33% accuracy.
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Soneji, H.N., Sudhanvan, S. (2021). Fake News Detection Using Convolutional Neural Networks and Random Forest—A Hybrid Approach. In: Gunjan, V.K., Suganthan, P.N., Haase, J., Kumar, A. (eds) Cybernetics, Cognition and Machine Learning Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6691-6_39
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DOI: https://doi.org/10.1007/978-981-33-6691-6_39
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