Optimized Multi Support Vector Machine Based Approach for Fake News Detection

Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country’s status or overstates the expense of special functions for a government, destroying democracy in certain countries. The project proposes an Multi Support Vector Machine (MSVM) -based approach for detecting fake news. The proposed model will be used to classify or detect the news as fake or real. Principal Component Analysis (PCA) is used for Feature Extraction. Principal Component Analysis (PCA) reduces the dimension of the data set comprising many related variables and recalls the maximum change in actual data. The proposed work will select the essential features with a Firefly-Optimized Algorithm (FA). The Firefly Optimized Algorithm (FA) is one of the various Evolutionary Algorithms (EAs) with various purposes. For the classification of fake news, an Multi Support Vector Machine (MSVM) classifier algorithm is implemented.