Fake news Detection on Social Media Using Machine Learning

Abstract

The spread of misinformation on the internet and social media has become a growing problem, impacting public opinion and decision-making. In this study, we explore how machine learning can be used to detect and classify fake news more effectively. We gathered a diverse dataset from various online sources and applied a series of preprocessing steps to prepare the data for analysis. This included techniques like tokenization, normalization, removing punctuation and stop words, and lemmatization. To improve the quality of the analysis, we developed an Enhanced Feature Engineering framework for Fake News Detection. This framework combined key features such as TF-IDF, Bag of Words, tweet length, and sentiment analysis, creating a robust dataset for training machine learning models. Among the various models we tested, the ensemble voting classifier stood out for its accuracy and reliability in distinguishing real news from fake. Its strong performance demonstrates the potential of combining multiple algorithms to tackle complex problems like misinformation.

Through this research, we aim to contribute to the ongoing efforts to combat fake news, helping to create a more reliable and trustworthy digital space.