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Reframing Responsibility: Framing-Aware Event Causality Identification
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:46
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
Reframing Responsibility: Framing-Aware Event Causality Identification
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.
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