@inproceedings{upravitelev-etal-2026-retrieving,
title = "Retrieving Climate Change Disinformation by Narrative",
author = {Upravitelev, Max and
Solopova, Veronika and
Jakob, Charlott and
Sahitaj, Premtim and
M{\"o}ller, Sebastian and
Schmitt, Vera},
editor = "Grasso, Francesca and
Basile, Valerio and
Bosco, Cristina and
Ibrohim, Muhammad Okky and
Skeppstedt, Maria and
Stede, Manfred",
booktitle = "Proceedings of the 2nd Workshop on Ecology, Environment, and Natural Language Processing",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2026.nlp4ecology-1.1/",
doi = "10.63317/5markfecdiyu",
pages = "1--14",
abstract = "Climate disinformation evolves faster than the fixed taxonomies used to detect it. Thus, we re-frame narrative detection as a retrieval task: given a narrative{'}s core message as a query, rank texts from a corpus by alignment with that narrative. This formulation requires no predefined label set and can accommodate emerging narratives. We repurpose three climate disinformation datasets (CARDS, Climate Obstruction, climate change subset of PolyNarrative) for retrieval evaluation and propose SpecFi, a framework that generates hypothetical documents to bridge the gap between abstract narrative descriptions and their concrete textual instantiations. SpecFi uses community summaries from graph-based community detection as few-shot examples for generation, achieving a MAP of 0.505 on CARDS without access to narrative labels. We further introduce narrative variance, an embedding-based difficulty metric, and show via partial correlation analysis that standard retrieval degrades on high-variance narratives (BM25 loses 63.4{\%} of MAP), while SpecFi-CS remains robust (32.7{\%} loss). Our analysis also reveals that unsupervised community summaries converge on descriptions close to expert-crafted taxonomies, suggesting that graph-based methods can surface narrative structure from unlabeled text."
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<abstract>Climate disinformation evolves faster than the fixed taxonomies used to detect it. Thus, we re-frame narrative detection as a retrieval task: given a narrative’s core message as a query, rank texts from a corpus by alignment with that narrative. This formulation requires no predefined label set and can accommodate emerging narratives. We repurpose three climate disinformation datasets (CARDS, Climate Obstruction, climate change subset of PolyNarrative) for retrieval evaluation and propose SpecFi, a framework that generates hypothetical documents to bridge the gap between abstract narrative descriptions and their concrete textual instantiations. SpecFi uses community summaries from graph-based community detection as few-shot examples for generation, achieving a MAP of 0.505 on CARDS without access to narrative labels. We further introduce narrative variance, an embedding-based difficulty metric, and show via partial correlation analysis that standard retrieval degrades on high-variance narratives (BM25 loses 63.4% of MAP), while SpecFi-CS remains robust (32.7% loss). Our analysis also reveals that unsupervised community summaries converge on descriptions close to expert-crafted taxonomies, suggesting that graph-based methods can surface narrative structure from unlabeled text.</abstract>
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%0 Conference Proceedings
%T Retrieving Climate Change Disinformation by Narrative
%A Upravitelev, Max
%A Solopova, Veronika
%A Jakob, Charlott
%A Sahitaj, Premtim
%A Möller, Sebastian
%A Schmitt, Vera
%Y Grasso, Francesca
%Y Basile, Valerio
%Y Bosco, Cristina
%Y Ibrohim, Muhammad Okky
%Y Skeppstedt, Maria
%Y Stede, Manfred
%S Proceedings of the 2nd Workshop on Ecology, Environment, and Natural Language Processing
%D 2026
%8 May
%I European Language Resources Association
%C Palma de Mallorca, Spain
%F upravitelev-etal-2026-retrieving
%X Climate disinformation evolves faster than the fixed taxonomies used to detect it. Thus, we re-frame narrative detection as a retrieval task: given a narrative’s core message as a query, rank texts from a corpus by alignment with that narrative. This formulation requires no predefined label set and can accommodate emerging narratives. We repurpose three climate disinformation datasets (CARDS, Climate Obstruction, climate change subset of PolyNarrative) for retrieval evaluation and propose SpecFi, a framework that generates hypothetical documents to bridge the gap between abstract narrative descriptions and their concrete textual instantiations. SpecFi uses community summaries from graph-based community detection as few-shot examples for generation, achieving a MAP of 0.505 on CARDS without access to narrative labels. We further introduce narrative variance, an embedding-based difficulty metric, and show via partial correlation analysis that standard retrieval degrades on high-variance narratives (BM25 loses 63.4% of MAP), while SpecFi-CS remains robust (32.7% loss). Our analysis also reveals that unsupervised community summaries converge on descriptions close to expert-crafted taxonomies, suggesting that graph-based methods can surface narrative structure from unlabeled text.
%R 10.63317/5markfecdiyu
%U https://aclanthology.org/2026.nlp4ecology-1.1/
%U https://doi.org/10.63317/5markfecdiyu
%P 1-14
Markdown (Informal)
[Retrieving Climate Change Disinformation by Narrative](https://aclanthology.org/2026.nlp4ecology-1.1/) (Upravitelev et al., NLP4Ecology 2026)
ACL
- Max Upravitelev, Veronika Solopova, Charlott Jakob, Premtim Sahitaj, Sebastian Möller, and Vera Schmitt. 2026. Retrieving Climate Change Disinformation by Narrative. In Proceedings of the 2nd Workshop on Ecology, Environment, and Natural Language Processing, pages 1–14, Palma de Mallorca, Spain. European Language Resources Association.