Investigating the Fake News Using Machine Learning Algorithms

The extensive volume of data available on online platforms, such as social media, poses challenges in our ability to recognize, evaluate, and rectify misleading information, commonly referred to as “fake news.” Ensuring the accuracy of information disseminated on the internet, particularly on social media, has gained increasing significance. In this document, we present a technique for detecting false information and explore potential applications across various social media platforms, including Twitter, Facebook, Instagram, and news websites. To create a supervised machine learning model capable of categorizing news articles as either accurate or false, we review prior research on fake news detection and investigate established machine learning algorithms, including Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, and Stochastic Gradient Descent. We propose utilizing Python's scikitlearn library to execute text preprocessing tasks like tokenization and feature extraction. This library provides valuable tools such as the Count Vectorizer and TF-IDF Vectorizer, which facilitate feature extraction and text data vectorization. Subsequently, based on the outcomes of the confusion matrix, we may apply feature selection methods to enhance model performance and identify the most influential features for achieving high precision. Our findings demonstrate that machine learning approaches can effectively tackle the issue of discerning false news.