Fake News Detection using Machine Learning with NLP

Abstract

With the advancement of technology, fake news has become a common trend. It has the ability to cause harm to citizens, protests, and even a threat to democracy. It gives agitators the ability to spread fake news or false propaganda, triggering civil unrest. Since then, identifying fake news has become a critical concern, with every company attempting to develop a system for detecting and combating it. Fake news has had a negative impact on society in terms of politics and culture. It has had a negative impact on both online and offline social networking networks, as well as groups and conversations. In this study, I propose a model for detecting false news that combines NLP and machine learning techniques. To determine which model produces the best results, I compare various classifier models and extraction techniques. Introduction, motivation, rationality of the study, research questions, expected output, terminologies, related works, comparative analysis, challenges, research methodology, statistical analysis, applied mechanism, results and discussion are briefly written here. Impact on society, environment and sustainability are also explained here. Tables and Figures are included and listed. Then pertinent information is repeated in the summary section for convenience. At the end of the report an annotated reference list is included for ease in finding other useful guidance.