Fake Tweet Data Analysis using Machine Learning Methods

As social networking websites have grown in popularity, so has the amount of data being generated online. M any people set up social media profiles to share their thoughts, images, and videos with the world. There are various ways for people to express themselves succinctly on Twitter, for instance. Twitter is a service that allows users to collaborate with one other. Tweets like this one might launch a worldwide movement for change or a global debate. It was necessary to classify them, and just the tweet itself could be posted. Kaggle's KFC challenge, dubbed “the M cDonald's of AI,” provided the data for this piece. Approximately 40000 tweets were analysed in this study. Cleaning the data using Inverse Term Frequency (TF-IDF). Clean data may be analysed using SVM, RF, and Decision Tree classification algorithms (DT). With an accuracy rate of 88.51%, the Decision Tree algorithm is the most reliable. The accuracy, recall, precision, and F1 of this result are evaluated.