A Review of Fake News Detection Methods using Machine Learning

The widespread use of social media has had a terrible impact on our society due to the spread of fake news. In particular, the author has seen the idea of the quality of fake news before its origin. Like the internet, publishers used false and misleading information to further their interest. People often get involved in social media as social media provides low cost, quick access, and fast spread of news. It has been seen for many years that fake news harms persons as well as society. So, the challenge of fake news detection arrived. Inappropriate news took place to attract people so the sender can start putting the rumor of news. This had led to a negative impact on people about social media for their news. This led to inconvenience for offline news as well because when people will too much depend on online platforms that will reduce the offline users. This survey deals with a review of existing machine learning algorithms Naïve Bayes, Convolutional Neural Network, LSTM, Neural Network, Support Vector Machine proposed for detecting and reducing fake news from different social media platforms like Facebook, whatsapp, twitter, etc. This review provides a comprehensive detail including data mining perspective, evaluation metrics, and representative datasheets. Further, a comparison of the state-of-the-art is presented and untackled challenges in detecting fake news are highlighted. Research in the field of detecting fake news has been hampered to a great extent by the lack of quantity and quality of existing datasets. Therefore, this review compares the existing approaches to build models and with further improvements to be expected by using the combination of different machine learning techniques.