Development of A Machine Learning-Based Model Using a Decision Tree for Detecting Fake News: Analyzing Techniques for Accurate Content Verification

Abstract

With the growing spread of information through social media and online news, identifying fake news has become increasingly important. To explore this issue, the Pew Research Center conducted surveys in the U.S. to examine how adults access news on social platforms, focusing on the behaviors and demographics of users who rely on these channels. This research sought to address a major gap in traditional fake news detection approaches, which were largely manual and lacked the sophistication of advanced machine learning and AI methods. Such conventional techniques struggle to handle the complexity and contextual manipulation of information, where accurate facts can be framed misleadingly. To overcome these shortcomings, the study developed a machine learning–based model to detect fake news by analyzing article content and recognizing patterns of misinformation. It utilized advanced natural language processing (NLP) techniques and supervised learning algorithms. Decision Trees (achieving 99.67% accuracy), Logistic Regression (99.13%), and Random Forest (99.15%). Processes such as tokenization and TF-IDF were applied to train the model on the ISO Fake News dataset, which combined real news from Reuters.com with fake news from unreliable sources flagged by PolitiFact and Wikipedia. Model performance was evaluated using accuracy, precision, recall, and F1-score, all reaching 99.67%, demonstrating exceptional detection capability. This work contributes to the field of machine learning by enhancing NLP methods and improving the effectiveness of fake news detection models. Future research is encouraged to expand datasets, incorporate multiple languages, employ deep learning like RNNs, CNNs, and Transformers (e.g., BERT and RoBERTa) for richer contextual understanding, and establish benchmarks based on real-world case studies.