Hybrid text classification method for fake news detection

Abstract

Fake news will be news, stories or scams made to purposely misguide or delude perusers. As a rule, these accounts are made to impact individuals' perspectives, push a political motivation or cause disarray and can regularly be a gainful business for online distributers. Fake news stories can swindle individuals by looking like believed sites or utilizing comparative names and web delivers to trustworthy news associations. The fake news detection has the three phases which are pre-processing, feature extraction and classification. In the previous time Support Vector Machine (SVM) classification is applied for the fake news detection. To improve accuracy of the fake news hybrid classification model is designed in this research work. The proposed model is implemented in Python and results are analyzed in terms of accuracy, precision and recall. Experimental analysis shows that the proposed method outperforms competitive techniques.