An Ensemble Modelling of Feature Engineering and Predictions for Enhanced Fake News Detection

Asowo, Patricia; Lal, Sangeeta; Ani, Uchenna

Abstract

The threat of fake news jeopardizing the credibility of online
news platforms, particularly on social media, underscores the need for innovative solutions. This paper proposes a creative engine for detecting fake news, leveraging advanced machine learning techniques, specifically Bidirectional En-coder Representations by Transformers (BERT). Our approach involves feature selection from news content and social contexts, combining predictions from multiple models, including Random Forest, BERT, GRU, LSTM, and a voting ensemble model. Through extensive evaluation of the WELFake dataset, our method highlights an impressive accuracy of 99%, surpassing baselines and existing systems. Our study highlights the crucial role of hyperparameter tuning, improving the performance of the BERT model to 100%

Citation

Asowo, P., Lal, S., & Ani, U. (2024, December). An Ensemble Modelling of Feature Engineering and Predictions for Enhanced Fake News Detection. Presented at AI-2024 Forty-fourth SGAI International Conference on Artificial Intelligence, CAMBRIDGE, ENGLAND

• Presentation Conference Type: Conference Paper (published)
• Conference Name: AI-2024 Forty-fourth SGAI International Conference on Artificial Intelligence
• Start Date: Dec 17, 2024
• End Date: Dec 19, 2024
• Acceptance Date: Aug 31, 2024
• Publication Date: 2025
• Deposit Date: Sep 12, 2024
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/978-3-031-77918-3_16
• Public URL: https://keele-repository.worktribe.com/output/920444