Detecting Fake News on Twitter by Using Artificial Intelligence

A great deal of information, particularly on social networking sites where individuals from all over the globe have continual and straightforward access to upload materials and messages, spreads rapidly throughout the world during the epidemic of Covid-19. The widespread dissemination of fake news may significantly impact people's mental and cognitive health. This case examines the dissemination of false information through Twitter during the coronavirus outbreak. This research presents a novel method for identifying misinformation spread over Tweets during the Covid-19 benchmark. The suggested technique is based on the combination of natural language processing, artificial intelligence, and deep learning, and it employs a categorization strategy that uses attributes unique to recent tweets. Apache Spark is used to implement the procedure in parallel. The experimental findings reveal that when combined with the randomly generated forest algorithm, this method produces extremely beneficial outcomes with an accuracy of 79%. It also shows that the tone of tweets is a crucial factor in identifying hoaxes. Indeed, this approach beats alternatives that do not consider contemporary tweets' unique qualities.