Fake News Detection Model

Abstract

The proliferation of fake news across social media and various other platforms is a significant concern due to its potential to cause widespread social and national harm. Extensive research efforts are currently underway to identify and combat this issue. This study conducts an in-depth analysis of research pertaining to the detection of fake news, examining traditional machine learning models to determine the most effective approach. By leveraging supervised machine learning algorithms, a model will be developed to classify news articles as either true or false. This model will utilize tools such as Python's scikit-learn and NLP for textual analysis. The process will involve feature extraction and vectorization, with a focus on utilizing Python's scikit-learn library for text data tokenization and feature extraction, leveraging tools like Count Vectorizer and Tiff Vectorizer. Additionally, feature selection methods will be employed to identify the most relevant features for optimal precision, as determined by the results of the confusion matrix