Much of the scientific findings, public discourse, and political framing are documented as text. Facing digitalization, these are now aggregated in newspaper repositories, social media platforms, and archives of peer-reviewed articles. This large volume of digitalized text data, combined with advancements in the field of natural language processing (NLP), has opened new scalable ways of extracting actionable information from texts. A recent development is the use of large language models (LLMs), which are capable of understanding and generating human-like texts. By leveraging these tools and datasets, we can reveal hidden patterns in texts and derive insights for managing the risks posed by multi-hazards that were previously unattainable.

In this lecture, I will present how unstructured text data (e.g. newspapers, reports, meeting minutes) can be used to obtain actionable information on the systemic socio-economic impacts of climate hazards as well as the response and adaptation measures adopted.

Three ways text can be used in multi-hazard and climate change-related research: discovery, measurement, and inference (based on Grimmer et al., 2022).