BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".