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Talk · Workshop on Natural Language Processing Meets Climate Change · January 1, 2026

CA-ClimateNews: A Human-Annotated Multilingual Benchmark for Climate-Relevance Classification of Central Asian News

Adilet Metinov, Zarina Uvalieva, Bektemir Kumarbai Uulu, Nurtilek Alibekov, Tynchtykbek Tashbaltaev

Adilet Baktiiarovich Metinov
Open via DOIPDF

Abstract

Climate change is acute in Central Asia (glacier loss, the Aral Sea, and drought exposure), yet there are no NLP resources for climate discourse in the region's low-resource Turkic languages.We present CA-ClimateNews, the first human-annotated multilingual dataset for climate-relevance classification of Central Asian news, covering Kyrgyz, Kazakh, Uzbek, and (regionally grounded) Russian.The corpus comprises 3,140 news items labeled at two levels (binary climate relevance and an 8way topic category) by two annotators per language, with a 100-item blind double-annotation overlap adjudicated to gold.Inter-annotator agreement is almost perfect (Cohen's κ 0.86-0.98 on relevance).Because we used transparent LLM-assisted pre-labeling, we additionally run a between-subjects experiment showing that displaying model pre-labels induces anchoring: an independent group shown the prelabel overrode it 3-7× less than a blind group.Our released labels are the blind version.We report baselines: fine-tuned multilingual encoders reach 88-96 relevance macro-F1 on the balanced benchmark; a Russian-only model loses up to ∼7 points transferring zero-shot to the Turkic languages even at matched training size (least, ∼2, for Kazakh), and a related Turkic source is no better, isolating a cross-lingual gap that in-language supervision closes.The dataset, guidelines, and code are publicly released.1

Topic ModelingSentiment Analysis and Opinion MiningText and Document Classification Technologiesclimate change discoursenews classificationmultilingual benchmark

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