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• Five BERT-based variants (BERT, RoBERTa, DeBERTa, DistilBERT, and ALBERT) were compared for generic news frame detection.method 95%
• The study introduces fine-tuned models capable of robustly performing generic news frame detection.method 95%
• A labelled generic news frames dataset based on the Swiss electoral context was created for testing contextual robustness.method 95%
• All BERT-based model architectures included a pre-trained transformer backbone, a pre-classifier layer, ReLU activation, dropout regularization, and a linear classification layer.method 90%
• DeBERTa achieved the highest macro F1 score of 0.72 among the tested models.result 85%
• RoBERTa performed worse overall compared to other models, despite specific class performance variations.result 80%
• The training data was augmented using OPUS-MT to balance classes, but this did not necessarily balance the diversity of textual material within each frame.limitation 85%

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