Delving into the Practices Involved in the Creation and Dissemination of Misinformation

Abstract

This study investigates the authenticity of news with specific training features validating the same with specific machine-learning techniques. The contents of fake news are created to make credible information that would create mass opinions and provide a strong basis to convince the readers or confuse them utterly. The fake information is usually disseminated using numerous automated algorithms. Therefore, it is very quintessential to identify the sources and authenticity of such information. With recent advancements in information communication technology, there exists a cluster of deep knowledge from which a user intends to retrieve relevant information such as news articles. For data mining and classification tasks such as fake news classification, the approach of machine learning can be employed for effective experimentation. To address the raised issues in this study, a comprehensive and diversified dataset was required that must contain relevant knowledge with sentiment tags such as authentic and fake news. To fulfill the same, a corpus comprising over 44k authentic and fake news items is collected. Moreover, this study emphasizes news classification as fake or authentic using data mining and analytics