Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Saptarshi Sengupta

Second Advisor

Nada Attar

Third Advisor

William Andreopoulos


Fake news, News articles, XGBoost, Naive Bayes, BERT, Machine learning


This project employs machine learning techniques to develop a sequential model for detecting and categorizing fake news, aiming to mitigate its proliferation in today's digital landscape. The model operates in two phases: in the first phase, the classification algorithms like Naïve Bayes, XGBoost and Random Forest are used to distinguish between true and false news stories and in the second phase the capabilities of Naïve Bayes, XGBoost, Random Forest, and the Transformer-based BERT (Bidirectional Encoder Representations from Transformers) model are leveraged to further categorize the news into specific topics.

The methodology encompasses several key steps: data acquisition, preprocessing, feature extraction, and model training, followed by evaluation using metrics such as accuracy, F1 score and a detailed classification report. These processes ensure that the model not only identifies fake news but also classifies it effectively alongside legitimate articles.

Available for download on Thursday, May 22, 2025