VeriFake: A TF-IDF and Ensemble Learning Framework for Web-Based Fake News Detection

The rapid circulation of fabricated news has become a serious problem for society, since it spreads confusion,
reinforces bias, and erodes public confidence in trustworthy
information. Because digital platforms and social networks allow
misleading stories to travel far more quickly than before, they
often shape opinions, awareness, and everyday decisions. Reliable
and automated detection tools are therefore needed to limit
this damage. This study presents a supervised machine learning
framework that recognizes fake news directly from website text.
Term Frequency Inverse Document Frequency (TF-IDF) is used
for feature extraction, and thirteen classifiers are trained and
compared, namely Random Forest (RF), Decision Tree (DT),
Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB),
Support Vector Machine (SVM), Linear Support Vector Classifier
(LSVC), K-Nearest Neighbor (KNN), Logistic Regression (LR),
XGBoost (XB), Multilayer Perceptron (MLP), Extra Trees (ET),
AdaBoost (AB), and Gradient Boosting (GB). The framework is
evaluated on a balanced Kaggle collection of 20,800 articles, split
evenly into 10,400 genuine and 10,400 false items. Random Forest
records the best accuracy at 99.23%, with LSVC and Decision
Tree following closely, while the remaining ensemble and boosting
methods also stay above 95%. For practical use, a secure Django
based web application was built so that users can submit news
text and receive an immediate prediction. The results confirm
that tree based ensembles and linear classifiers, when paired
with TF-IDF features, remain highly effective for monolingual
fake news detection, and the deployed platform shows how these
models can be turned into usable tools against misinformation.