Abstract
In recent years, there has been significant growth in the popularity of online social networks, with a corresponding increase in the volume of information shared over the web. Text, audio, and video content have been used as tools for spreading fake news on social networks, making it difficult to detect. In this paper, a fake news detection technique that uses genetic search for neural architecture selection and deep learning to classify news instances is proposed. From experimental results, our model achieved a detection rate of 89.6%, a false positive rate of 0.2, and a loss of 0.0982837, which is close to 0. To further verify our claims, statistical analysis showed a mean squared error of 0.258974358974359 and a root mean square error of 0.5088952337901771. These error rates are low relative to the size of the input and prove the effectiveness of our approach for fake news detection.
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