  Due to the evolution of the Web and social network platforms it becomes very
easy to disseminate the information. Peoples are creating and sharing more
information than ever before, which may be misleading, misinformation or fake
information. Fake news detection is a crucial and challenging task due to the
unstructured nature of the available information. In the recent years,
researchers have provided significant solutions to tackle with the problem of
fake news detection, but due to its nature there are still many open issues. In
this paper, we have proposed majority voting approach to detect fake news
articles. We have used different textual properties of fake and real news. We
have used publicly available fake news dataset, comprising of 20,800 news
articles among which 10,387 are real and 10,413 are fake news labeled as binary
0 and 1. For the evaluation of our approach, we have used commonly used machine
learning classifiers like, Decision Tree, Logistic Regression, XGBoost, Random
Forest, Extra Trees, AdaBoost, SVM, SGD and Naive Bayes. Using the
aforementioned classifiers, we built a multi-model fake news detection system
using Majority Voting technique to achieve the more accurate results. The
experimental results show that, our proposed approach achieved accuracy of
96.38%, precision of 96%, recall of 96% and F1-measure of 96%. The evaluation
confirms that, Majority Voting technique achieved more acceptable results as
compare to individual learning technique.
