Causal Micro-Narratives

Mourad Heddaya, Qingcheng Zeng, Alexander Zentefis, Rob Voigt, Chenhao Tan


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.
Anthology ID:
2024.wnu-1.12
Volume:
Proceedings of the The 6th Workshop on Narrative Understanding
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yash Kumar Lal, Elizabeth Clark, Mohit Iyyer, Snigdha Chaturvedi, Anneliese Brei, Faeze Brahman, Khyathi Raghavi Chandu
Venue:
WNU
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
67–84
Language:
URL:
https://aclanthology.org/2024.wnu-1.12
DOI:
10.18653/v1/2024.wnu-1.12
Bibkey:
Cite (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.
Cite (Informal):
Causal Micro-Narratives (Heddaya et al., WNU 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.wnu-1.12.pdf