Supervisor
Dr. Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
This project investigates how Artificial Intelligence (AI), specifically supervised machine learning techniques, can be applied to detect and classify fake news with high accuracy. The motivation stems from the widespread dissemination of misinformation on social media, where false narratives often spread faster than verified content. To address this issue, two distinct classification models were implemented and evaluated: one combining TF-IDF vectorization with Random Forest classifier, and another using TF-IDF with Logistic Regression. The TF-IDF technique was used to convert raw textual data into meaningful numerical features, capturing word frequency and relevance within the corpus. Both models were trained and tested on a balanced dataset of true and fake news articles. Their performance was compared based on key metrics such as accuracy, precision, recall, and F1-score. The findings revealed that the Random Forest model consistently outperformed Logistic Regression, particularly when trained on uncleaned (raw) text. This suggests that Random Forest is more robust to linguistic noise, making it a more suitable choice for deployment in real-time fake news detection systems, especially on user-generated content platforms like social media.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Poster
Resource Type
other
Recommended Citation
Lambert, G., & Varela, I.
(2025) Detecting Fake News Using AI CCT College Dublin.
DOI: https://doi.org/10.63227/652.199.46