Development of a Machine Learning-Based Model In Detecting Fake News: Analyzing Techniques For Accurate Content Verification - Tomba, Esther Kinkosi; Muriira, Lawrence Mwenda; Anondo, Timothy (International Journal of Professional Practice, 2025)
Information through social media and other news outlets made detecting fake news crucial for individuals. The Pew Research Centre conducted surveys in the U.S.A to examine how adults consume news via social media, aiming to understand the behaviours and demographics of those relying on such platforms. This study addressed a critical gap in traditional fake news detection methods, which mainly used manual approaches and lacked advanced machine learning or AI techniques. Traditional methods are insufficient to handle the complexity, and contextual manipulation, where accurate information is presented misleadingly. To overcome these limitations, the study developed a ML Based model for detecting fake news, by analysing article content, and identifying patterns of misinformation. It employed advanced natural language processing techniques and supervised learning algorithms such as Decision Trees with 99.67% of accuracy, Logistic Regression with 99.13%, and Random Forest with 99.15%. Methods like Tokenization and TF-IDF were used to train the model using the ISO Fake news dataset. This dataset included real news from Reuters.com and fake news from unreliable sources flagged by PolitiFact and Wikipedia. Additional labelled datasets like LIAR and FakeNewsNet, along with newly gathered data, were used to supplement the training. Model performance was assessed using accuracy, precision, recall and F1-Score, all achieving 99.67%, demonstrating superior detection capabilities. The research contributed to ML by advancing NLP Techniques and improving fake news detection models. The study recommends future researchers, engineers and all those involved in developing machine learning systems to enhance further effectiveness should expand datasets and including diverse languages, applying deep learning models like RNN, CNN, and Transformers, (e.g., BERT, ROBERTa) for better contextual analysis, and establishing benchmarks using real-world case studies.