Ensemble Meta-Learning Framework with BERT for Fake News Detection in Social Media

doi:10.23940/ijpe.26.01.p5.4049

Abstract

Abstract: If social media platforms are to serve as the primary sources of information, then this type of news article tends to weaken credibility and factual information significantly. The concept of this paper is to contribute to this issue by applying the current NLP technique built with meta-learning and the BERT model to identify synthetic content in the ISOT fake news dataset. The study explores the potential of Meta-learning with BERT in developing practical tools for analysis and improving fake news detection. It also highlights certain limitations related to the use of this technology, emphasizing the need for accountability. This, in turn, underscores the importance of responsible design and optimization to maximize the benefits of advanced artificial intelligence. An ensemble transformer-based model should be used in fake news analysis and detection, as it yielded the highest performance accuracy of 98.29% in this context.

Key words: fake news detection, ensemble transformer model, meta learning, Bert-base model, ISOT dataset

Umoru Yahaya Ibrahim, Rajesh Prasad, Bisallah Hashim Ibrahim, and Ogwueleka Francisca Nonyelum. Ensemble Meta-Learning Framework with BERT for Fake News Detection in Social Media [J]. Int J Performability Eng, 2026, 22(1): 40-49.

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