International Journal of Advanced Innovative Technology in Engineering (IJAITE)

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Fake News Detection Based on Machine Learning Approach

Karishma K. Misal, Prof. Minakshi Ramteke

Abstract :

Fake news detection is an interesting topic for computer scientists and social science. Because of the recent growth of online social media fake news has a great impact on society. There is much information from disparate sources among various users around the world. Twitter is one of the most popular applications that are able to deliver appealing data in a timely manner. Developing a technique that can detect fake news from Twitter is becoming a necessary and challenging task. This paper proposes a machine learning method that can identify fake news from Kaggle data. The experiment is carried out with four widely used machine learning methods such as Logistic Regression, Naïve Bayes, Gradient Boost, and SVM using the data collected. The results show that all machine learning methods can detect fake news in this data set accurately. This investigated the four methods and compared their accuracies. The model that achieves the highest accuracy is gradient boost and the highest accuracy score is 99.61%.

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