Fake News Detection Using Data Science Approaches

In today’s internet-driven world, fake news is a problem that is only becoming worse. Given the ease of exchanging information online, separating false information from reliable information is a crucial endeavor. Using bag-of-words and consecutive mining approaches, we provide a data mining solution in this work to categorize articles as genuine or fake. We also compare the accuracy of the solution for identifying fake news across different datasets. Our method first purifies the input information by normalizing words and eliminating “filler” words. The cleansed data is then vectorized using sequential mining techniques. After that, it uses vectorized data to train the classification models and categorizes unknown news as authentic or fake. Assessment of our technology to mine and categorize bogus news using actual data demonstrates its viability. The classification algorithms are then trained using vectorized data to categorize unreported news as real or bogus. The effectiveness of our technology in identifying and categorize bogus news has been evaluated using real-world data.