Detecting and Mitigating the Dissemination of Fake News : Challenges and Future Research Opportunities

With increasing popularity in the use of social media for news consumption, the substantial widespread dissemination of fake news has the potential to adversely affect individuals as well as the society as a whole. Even in the midst of the current covid-19 pandemic, false information shared on websites such as WhatsApp, Twitter, and Facebook have the potential to cause panic and shock a large number of people in various parts of the world. These misconceptions obscure healthier habits and encourage incorrect procedures, which aid in the transmission of the virus and, as a result, result in poor physical and psychological health results for individuals. Therefore, it is a research challenge to validate the source, content and publisher of a news article for classifying it as genuine or fake. The existing systems and techniques are not efficient enough to accurately classify a given news based on its statistical rating. Machine learning plays an imperative part in categorizing news data and information, despite some limitations. Our project not only aims on fake news detection but also on generation of real news once the fake news is detected. We propose a user-friendly webpage on which the user enters the news article statement. It is then tested by our machine learning algorithm which then classifies it as genuine or fake, after which the important words are extracted from the statement which helps to get the corresponding genuine news by scraping it from trusted sources and show it to the user. We have compared two machine learning algorithms in this which are- Passive Aggressive Classifier and Naïve Bayes algorithm. We got an accuracy of about 93.5% from Passive Aggressive Classifier and about 83.5% from Naïve Bayes algorithm.