Fake News Detection Using Machine Learning

Fake News Detection Using Machine Learning

ABSTRACT–False stories on social media and various other platforms spread rumors and are a source of great concern as they have the potential to wreak havoc on the social and social ills with devastating consequences. A lot of research is going on just to focus on finding it. This paper analyzes research related to the detection of false information and can examine the culture of machine learning models to select the best to create a product model with a supervised machine learning algorithm, which can distinguish or find false stories true or false, using tools such as python scikit-learn, i -NPL text analysis. This process will lead to feature removal and vectorization; we should use the python scikit-learn library to make tokens and extract the text data feature, as this library contains useful tools such as Count Vectorizer and Tiff Vectorizer. Then we will create methods to select test features and select advanced file features to get the highest accuracy, depending on the results of the confusion matrix.

Keyword: count vectorizer, NLP, precision.