@inproceedings{heddaya-etal-2024-causal,
title = "Causal Micro-Narratives",
author = "Heddaya, Mourad and
Zeng, Qingcheng and
Zentefis, Alexander and
Voigt, Rob and
Tan, Chenhao",
editor = "Lal, Yash Kumar and
Clark, Elizabeth and
Iyyer, Mohit and
Chaturvedi, Snigdha and
Brei, Anneliese and
Brahman, Faeze and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the The 6th Workshop on Narrative Understanding",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wnu-1.12",
doi = "10.18653/v1/2024.wnu-1.12",
pages = "67--84",
abstract = "We present a novel approach to classify causal micro-narratives from text. These narratives are sentence-level explanations of the cause(s) and/or effect(s) of a target subject. The approach requires only a subject-specific ontology of causes and effects, and we demonstrate it with an application to inflation narratives. Using a human-annotated dataset spanning historical and contemporary US news articles for training, we evaluate several large language models (LLMs) on this multi-label classification task. The best-performing model{---}a fine-tuned Llama 3.1 8B{---}achieves F1 scores of 0.87 on narrative detection and 0.71 on narrative classification. Comprehensive error analysis reveals challenges arising from linguistic ambiguity and highlights how model errors often mirror human annotator disagreements. This research establishes a framework for extracting causal micro-narratives from real-world data, with wide-ranging applications to social science research.",
}
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<abstract>We present a novel approach to classify causal micro-narratives from text. These narratives are sentence-level explanations of the cause(s) and/or effect(s) of a target subject. The approach requires only a subject-specific ontology of causes and effects, and we demonstrate it with an application to inflation narratives. Using a human-annotated dataset spanning historical and contemporary US news articles for training, we evaluate several large language models (LLMs) on this multi-label classification task. The best-performing model—a fine-tuned Llama 3.1 8B—achieves F1 scores of 0.87 on narrative detection and 0.71 on narrative classification. Comprehensive error analysis reveals challenges arising from linguistic ambiguity and highlights how model errors often mirror human annotator disagreements. This research establishes a framework for extracting causal micro-narratives from real-world data, with wide-ranging applications to social science research.</abstract>
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%0 Conference Proceedings
%T Causal Micro-Narratives
%A Heddaya, Mourad
%A Zeng, Qingcheng
%A Zentefis, Alexander
%A Voigt, Rob
%A Tan, Chenhao
%Y Lal, Yash Kumar
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brei, Anneliese
%Y Brahman, Faeze
%Y Chandu, Khyathi Raghavi
%S Proceedings of the The 6th Workshop on Narrative Understanding
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F heddaya-etal-2024-causal
%X We present a novel approach to classify causal micro-narratives from text. These narratives are sentence-level explanations of the cause(s) and/or effect(s) of a target subject. The approach requires only a subject-specific ontology of causes and effects, and we demonstrate it with an application to inflation narratives. Using a human-annotated dataset spanning historical and contemporary US news articles for training, we evaluate several large language models (LLMs) on this multi-label classification task. The best-performing model—a fine-tuned Llama 3.1 8B—achieves F1 scores of 0.87 on narrative detection and 0.71 on narrative classification. Comprehensive error analysis reveals challenges arising from linguistic ambiguity and highlights how model errors often mirror human annotator disagreements. This research establishes a framework for extracting causal micro-narratives from real-world data, with wide-ranging applications to social science research.
%R 10.18653/v1/2024.wnu-1.12
%U https://aclanthology.org/2024.wnu-1.12
%U https://doi.org/10.18653/v1/2024.wnu-1.12
%P 67-84
Markdown (Informal)
[Causal Micro-Narratives](https://aclanthology.org/2024.wnu-1.12) (Heddaya et al., WNU 2024)
ACL
- Mourad Heddaya, Qingcheng Zeng, Alexander Zentefis, Rob Voigt, and Chenhao Tan. 2024. Causal Micro-Narratives. In Proceedings of the The 6th Workshop on Narrative Understanding, pages 67–84, Miami, Florida, USA. Association for Computational Linguistics.