Enhancing Fake News Detection with Federated Learning and Word Embedding

Arunachalam, Ramanathan (2024) Enhancing Fake News Detection with Federated Learning and Word Embedding. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research paper explores the effectiveness of combining word embedding techniques and federated learning in fake news detection. The research was conducted using various datasets and three machine learning models (LSTM, CNN, and BERT) were trained using four different word embedding techniques (Word2Vec, Glove, FastText, and Doc2Vec). The results demonstrate that LSTM and CNN models, when combined with either Word2Vec or Doc2Vec, can significantly improve fake news detection accuracy. Federated learning also had a positive impact on accuracy. The research has several key contributions: 1) It demonstrates the effectiveness of combining word embedding techniques and federated learning in fake news detection. 2) It identifies the most effective word embedding techniques and machine learning models for fake news detection. 3) It suggests future directions for research on fake news detection.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Razzaq, Abdul UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Tamara Malone
Date Deposited:	03 Apr 2025 16:29
Last Modified:	03 Apr 2025 16:29
URI:	https://norma.ncirl.ie/id/eprint/7358

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