Causal explanations in political narratives are often framed and contested.Different sources may explain the same event by assigning responsibility to different actors, expressing different 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.The dataset and code are publicly available.1

News articles often describe the same realworld 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 Framingdivergent 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.The dataset and code will be made publicly available.1

Jin Zhao, Jingxuan Tu, Bingyang Ye, Xinrui Hu, Nianwen Xue, James Pustejovsky. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets.

Framing is used to present some selective aspects of an issue and make them more salient, which aims to promote certain values, interpretations, or solutions (Entman, 1993).This study investigates the nuances of media framing on public perception and understanding by examining how events are presented within news articles.Unlike previous research that primarily focused on word choice as a framing device, this work explores the comprehensive narrative construction through events and their relations.Our method integrates event extraction, Cross-Document Event Coreference (CDEC), and causal relationship among events to extract framing devices employed by the media to assess their role in framing the narrative.We evaluate our approach with a media attitude detection task and show that the use of event mentions, event cluster descriptors, and their causal relations effectively captures the subtle nuances of framing, thereby providing deeper insights into the attitudes conveyed by news articles.The experimental results show the framing device models surpass the baseline models and offer a more detailed and explainable analysis of media framing effects.We make the source code and dataset publicly available.1