Fake Data Analysis and Detection Using Ensembled Hybrid Algorithm

Abstract

Fake data detection is the most important problem to be addressed in the recent years, there is lot of research going on in this field. Because of its serious impacts on the readers. researchers, government and private agencies working together to solve the issue. This paper represents a hybrid approach for fake data detection using the multinomial voting algorithm. This algorithm was tested with multiple fake news dataset which resulted in an accuracy score of 94 percent which is a benchmark in the machine learning field where the other algorithms are at a range of 82 to 88 percent. The list of algorithms that have been used here is as follows Naive Bayes, Random Forest, Decision Tree, Support Vector Machine, K Nearest Neighbors. All these algorithms use training data as the bag of words model which was created using Count Vectorizer. Experimental data has collected from the Kaggle data world. Python is used as a language to verify and validate the results. Tableau is used as a visualization tool. Implementation is carried out using default algorithm values.