Abstract
A social media adoption is important to provide content authenticity and awareness for the unknown news that might be fake. Therefore, a Natural Language Processing (NLP) model is required to identify the content properties for language-driven feature generation. The present research work utilizes language-driven features that extract the grammatical, sentimental, syntactic, readable features. The feature from the particular news content is extracted to deal with the dimensional problem as the language level features are quite complex. Thus, the Dropout layer-based Long Short Term Network Model (LSTM) for sequential learning achieved better results during fake news detection. The results obtained validate the important features extracted linguistic model features and are combined to achieve better classification accuracy. The proposed Drop out based LSTM model obtained accuracy of 95.3% for fake news classification and detection when compared to the sequential neural model for fake news detection.
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