In studies of media coverage of extreme climate events, NLP methods have become indispensable for identifying relevant texts in large news databases. Still, enough annotated data to train accurate deep learning-based classifiers from scratch is often not available. Topic Models have the advantage of being both unsupervised and interpretable, but are typically used only for exploratory analysis or data characterisation. In this study, we investigate how to employ Topic Models as binary classifiers for refining the retrieval of relevant news about seven types of extreme climate events in the German media. Our method relies on the posterior distributions estimated by Topic Models to select relevant documents, without modifying their training procedure. Using an annotated sample to guide the evaluation, we show that the probabilities assigned to keywords used to query news databases can also be informative for selecting relevant topics and improve sample precision. We compare our results to a fine-tuned text embedding classifier and an open-weight LLM, discussing observed trade-offs, e.g. the LLM’s lowest precision. Moreover, we show that results are hazard-dependent, which speaks against considering climate events as a single category in NLP tasks.

Slow-onset events such as droughts lack a clearly defined onset, and systematic databases do not capture their impacts at fine scales. News reports can help fill this gap by providing information on how drought-driven water shortages affect communities and how authorities respond. Yet, extracting structured information from text remains challenging. Here, we use news articles and official public communications on water-saving recommendations and water-use restrictions in Sweden as a case study to benchmark LLMs for drought-related information extraction. We compare the proprietary GPT-5 series against three large open-access models (Llama3.3, Deepseek, and GPT-OSS) in extracting variables such as the date, location, and causes of water-saving measures. Using prompt engineering, structured outputs, and minimal post-processing, we evaluate performance using a test set of 210 manually annotated articles. The GPT-5 model series outperforms all open-access models across all variables. We find that, without task-specific fine-tuning, open-source models are sometimes outperformed by a keyword-based baseline, suggesting that general-purpose capabilities alone are insufficient for reliably extracting structured data. These findings underscore the challenges of monitoring water-shortage risks based on texts, particularly when extracting complex variables.

Landslides often hit newsstands due to their destructive and potentially fatal effects. News are a valuable source of information for creating or enriching disaster databases and for expediting media-based studies of the dynamics of media attention. To accomplish that, news datasets must be filtered, geolocated and validated. This paper focuses on how landslides around the world are reported in German newspapers. We analyse almost 55k news articles about 4.5k news events in a 25-year period, compare it with external measures of countries’ susceptibility to landslides and provide insights, e.g. the overreporting of Southern and Western Europe, to foster further studies on inequalities in media attention to international disasters.

To better understand how extreme climate events impact society, we need to increase the availability of accurate and comprehensive information about these impacts. We propose a method for building large-scale databases of climate extreme impacts from online textual sources, using LLMs for information extraction in combination with more traditional NLP techniques to improve accuracy and consistency. We evaluate the method against a small benchmark database created by human experts and find that extraction accuracy varies for different types of information. We compare three different LLMs and find that, while the commercial GPT-4 model gives the best performance overall, the open-source models Mistral and Mixtral are competitive for some types of information.