# ClimaText: A Dataset for Climate Change Topic Detection

arXiv: https://arxiv.org/abs/2012.00483 · HTML: https://mlsanity.com/paper/2012.00483/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 22 of 22)

- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Assessing Large Language Models on Climate Information](https://mlsanity.com/paper/2310.02932/) (2023)
- [WeatherQA: Can Multimodal Language Models Reason about Severe Weather?](https://mlsanity.com/paper/2406.11217/) (2024)
- [ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets](https://mlsanity.com/paper/2310.08096/) (2023)
- [ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures](https://mlsanity.com/paper/2406.09818/) (2024)
- [Environmental Claim Detection](https://mlsanity.com/paper/2209.00507/) (2022)
- [Towards Answering Climate Questionnaires from Unstructured Climate Reports](https://mlsanity.com/paper/2301.04253/) (2023)
- [EarthSE: A Benchmark for Evaluating Earth Scientific Exploration Capability of LLMs](https://mlsanity.com/paper/2505.17139/) (2025)
- [Don't Think Twice! Over-Reasoning Impairs Confidence Calibration](https://mlsanity.com/paper/2508.15050/) (2025)
- [DEEPAG'E: Answering Questions in Portuguese about the Brazilian Environment](https://mlsanity.com/paper/2110.10015/) (2021)
- [ClimateNLP: Analyzing Public Sentiment Towards Climate Change Using Natural Language Processing](https://mlsanity.com/paper/2310.08099/) (2023)
- [Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach](https://mlsanity.com/paper/2302.01887/) (2023)
- [Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks](https://mlsanity.com/paper/2303.13373/) (2023)
- [ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?](https://mlsanity.com/paper/2311.17107/) (2023)
- [EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection](https://mlsanity.com/paper/2404.05133/) (2024)
- [Retrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German News](https://mlsanity.com/paper/2605.03450/) (2026)
- [Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures](https://mlsanity.com/paper/2312.17337/) (2023)
- [Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change](https://mlsanity.com/paper/2505.18653/) (2025)
- [ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics](https://mlsanity.com/paper/2504.19066/) (2025)
- [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026)
- [Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining](https://mlsanity.com/paper/2609.07798/) (2026)

## Abstract (arXiv)

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce textscClimaText, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT citedevlin2018bert can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.