# Assessing socio-economic climate impacts from text data

arXiv: https://arxiv.org/abs/2605.20793 · HTML: https://mlsanity.com/paper/2605.20793/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 98 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 14 of 14)

- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP](https://mlsanity.com/paper/2109.06598/) (2021)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [A Pragmatic Guide to Geoparsing Evaluation](https://mlsanity.com/paper/1810.12368/) (2018)
- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Twitter Speaks: A Case of National Disaster Situational Awareness](https://mlsanity.com/paper/1903.02706/) (2019)
- [Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers](https://mlsanity.com/paper/2007.04524/) (2020)
- [Mordecai 3: A Neural Geoparser and Event Geocoder](https://mlsanity.com/paper/2303.13675/) (2023)
- [Leave no Place Behind: Improved Geolocation in Humanitarian Documents](https://mlsanity.com/paper/2309.02914/) (2023)
- [Bias in, Bias out: Annotation Bias in Multilingual Large Language Models](https://mlsanity.com/paper/2511.14662/) (2025)
- [TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data](https://mlsanity.com/paper/2212.04001/) (2022)
- [Data Quality Assessment: Challenges and Opportunities](https://mlsanity.com/paper/2403.00526/) (2024)
- [FAIR 2.0: Extending the FAIR Guiding Principles to Address Semantic Interoperability](https://mlsanity.com/paper/2405.03345/) (2024)

## Built on by (most cited first, 1 of 1)

- [The Course of News Events: A Comparison of Bottom-Up and Top-Down Approaches for Collecting Text-Based Data about Disasters](https://mlsanity.com/paper/2607.00849/) (2026)

## Abstract (arXiv)

Recent advances in natural language processing (NLP) and large language models (LLMs) have enabled the systematic use of large-scale textual data from news, social media, and reports to create datasets with socio-economic impacts of climate hazards such as floods, droughts, storms, and multi-hazard events. As the field of text-as-data for impact assessment expands, so does its methodological complexity. Yet research remains fragmented, with no clear guidelines for defining what constitutes an impact, handling temporal and spatial biases, and selecting appropriate modeling and post-processing strategies. This lack of coherence limits transparency and comparability across studies. Here, we address this gap by synthesising common practices, describing key challenges specific to the use of text-as-data methods for analyzing socio-economic impact data, and proposing recommendations to address them. By providing guidance on best practices, we aim to support the construction of robust text-derived socio-economic impact datasets that can more accurately inform disaster risk management and attribution studies.