ETD

• algorithm
• article
• femicide
• framing
• perpetrator justification
• tone
• victim blaming

This thesis proposes an automated and interpretable framework for the analysis of media framing in Italian news articles on femicide, combining linguistically grounded rule-based indicators with supervised learning for validation.
The framework operationalizes three key framing dimensions recurrent in journalistic guide lines and prior literature: victim blaming, perpetrator justification, and article tone. These dimensions are modeled through syntactic and semantic indicators derived from lightweight lin guistic preprocessing and interpretable lexical patterns. To avoid dependence on manual ground truth, labels are induced via a conservative multi-engine consensus strategy inspired by weak supervision principles.
The reliability of the proposed indicators is assessed through inter-source agreement and learnability analyses, comparing supervised models trained on automatically induced labels with models trained on a manually annotated subset. Both linear TF–IDF classifiers and transformer based models are evaluated, while post-hoc explainability methods are used to inspect linguistic signals supporting model decisions. Results show that the proposed framework captures stable and meaningful framing patterns at corpus level, supporting scalable and reproducible analysis of femicide framing in Italian journalism.