Detection of the Fake News Using Machine Learning Algorithms and Data Analysis Techniques
Due to the rapid advancement of online social networks in recent years, the prevalence of fake news has increased significantly. Fake news is deliberately created to deceive users by imitating real news, making it challenging to identify early on. So, we need to explore the accompanying information to improve its disclosure such as the publisher. This study focuses on analyzing and investigating various traditional machine learning models to determine the most effective one. The goal is to develop a supervised machine learning algorithm that can classify news articles as either true or fake, utilizing tools like Python‘s scikit-learn and NLP for text analysis. The proposed approach involves feature extraction and vectorization. To accomplish this, the scikit-learn library in Python is utilized, which offers helpful tools like CountVectorizer and TfidfVectorizer. The experiment involved implementing well-known algorithms: Logistic regression, Neural networks and SVM, and comparing their performance to determine the most suitable one. Each of the three algorithms performed well, but SVM demonstrated superior outcomes across nearly all categories.