Reframing Responsibility: Framing-Aware Event Causality Identification

ACL ARR 2026 January Submission8033 Authors

Keywords: event causality identification, computational framing, causal attribution, responsibility attribution, event-centric framing, political narratives, cross-document event coreference, discourse analysis, interpretability

Abstract:

Causal explanations in political narratives are often framed and contested. Different sources may explain the same event by assigning responsibility to different actors and expressing varying levels of certainty. Standard Event Causality Identification (ECI) focuses on detecting causal links and does not capture these distinctions.
We introduce Framing-Aware Event Causality Identification (FrECI), a framing-aware extension of ECI that models causal explanations as structured claims including responsibility targets, evaluative framing, source type, and epistemic modality grounded in established framing theories. We construct a multilingual dataset aligned across English, Chinese, and Arabic narratives using shared event anchors.
We evaluate FrECI using prompt-based large language model baselines and supervised neural models. Results show that prompt-based baselines struggle to recover complete framed causal claims, while joint supervised models perform substantially better. Finally, we demonstrate that FrECI enables quantitative analysis of divergent causal attribution across narratives.

Paper Type: Long

Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good

Research Area Keywords: event extraction, document-level extraction, frame detection and analysis, quantitative analyses of news and/or social media

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

Languages Studied: English, Chinese, Arabic

Submission Number: 8033