Fakenews AI: Identifying and Flagging Misinformation through Artificial Intelligence

Misinformation has become a pervasive problem in today's society. With the rise of social media and the ease of sharing information, false or misleading information can spread rapidly and have a significant impact on public opinion and decision-making. The challenge of identifying and flagging misinformation has become increasingly difficult, as conventional methods of fact-checking and verification struggle to keep up with the scale and speed of information dissemination. In this study, the author will explore the function of artificial intelligence (AI) in identifying and flagging misinformation, and the potential implications and future directions of the technology, looking at different types of models like Naive Bayes, LR, DT, RF, SVM, and LSTM. The author has tested how well they worked using measures like accuracy, precision, recall, and F1-score. The author has observed that the Decision Tree model is the best one overall. It had the highest accuracy and did the best job at classifying things compared to the other models.