Seeing the Same Story Differently: Framing‑Divergent Event Coreference for Computational Framing Analysis

ACL ARR 2025 May Submission5658 Authors

Abstract:

News articles often describe the same real-world event in strikingly different ways, shaping perception through framing rather than factual disagreement. However, traditional computational framing approaches often rely on coarse-grained topic classification, limiting their ability to capture subtle, event-level differences in how the same occurrences are presented across sources.
We introduce Framing-divergent Event Coreference (FrECo), a novel task that identifies pairs of event mentions referring to the same underlying occurrence but differing in framing across documents to provide a event-centric lens for computational framing analysis.
To support this task, we construct the high-agreement and diverse FrECo corpus. We evaluate the FrECo task on the corpus through supervised and preference-based tuning of large language models, providing strong baseline performance.
To scale beyond the annotated data, we develop a bootstrapped mining pipeline that iteratively expands the training set with high-confidence FrECo pairs. Our approach enables scalable, interpretable analysis of how media frame the same events differently, offering a new lens for contrastive framing analysis at the event level.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: frame detection and analysis, coreference resolution

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English

Submission Number: 5658