BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation

• Published in: Big data and cognitive computing Vol. 8; no. 8; p. 93
• Main Authors: Alnabhan, Mohammad Q., Branco, Paula
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Algorithms; Artificial intelligence; class imbalance; Computational linguistics; Crime; Datasets; deep learning; Detectors; Disinformation; domain classification; Election results; fake news; False information; Heuristic; Language processing; Machine learning; Natural language interfaces; Neural networks; News; Pandemics; Performance evaluation; Politics; Presidential elections; Social networks; Germany; United States > US
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc8080093

In an era where misinformation and fake news undermine social well-being, this work provides a complete approach to multi-domain fake news detection. Multi-domain news refers to handling diverse content across various subject areas such as politics, health, research, crime, and social concerns. Recognizing the lack of systematic research in multi-domain fake news detection, we present a fundamental structure by combining datasets from several news domains. Our two-tiered detection approach, BERTGuard, starts with domain classification, which uses a BERT-based model trained on a combined multi-domain dataset to determine the domain of a given news piece. Following that, domain-specific BERT models evaluate the correctness of news inside each designated domain, assuring precision and reliability tailored to each domain’s unique characteristics. Rigorous testing on previously encountered datasets from critical life areas such as politics, health, research, crime, and society proves the system’s performance and generalizability. For addressing the class imbalance challenges inherent when combining datasets, our study rigorously evaluates the impact on detection accuracy and explores handling alternatives—random oversampling, random upsampling, and class weight adjustment. These criteria provide baselines for comparison, fortifying the detection system against the complexities of imbalanced datasets.