Tools
Tools
Naveed, Raja Muhammad (2024) Leveraging Random Forrest for Fake News Detection on Social Media. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Preview |
PDF (Configuration Manual)
Download (874kB) | Preview |
Abstract
The rise of technology and increasing access to the Internet and social media (SM) platforms have made it easier to disseminate information globally. Social media platforms have become a primary source of spreading news. Individuals, governments, and organizations are using these platforms to increase their reach to target audiences. While it is an advantage of the internet and social media, many entities are abusing the facility to spread misinformation, resulting in chaos and disorder. This research project aims to determine fake news in SM posts to enhance the reliability of online information. The research intends to use a dataset comprising real and fake news articles from Kaggle. Machine learning techniques will classify the articles.
The research process will leverage Random Forest as a primary model for this project whereas Naive Bayes & Decision Tree will serve as baseline models. The models were trained and evaluated based on precision, accuracy, recall, and F1 score. This research will highlight the effectiveness of machine learning models in determining between fake and real news content with maximum accuracy and other parameters. The results provide a foundational framework for developing reliable tools to combat misinformation and contribute toward minimizing the spread of fake news in digital media.
Actions (login required)
 |
View Item |