# CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding

arXiv: https://arxiv.org/abs/2005.06627 · HTML: https://mlsanity.com/paper/2005.06627/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 25 of 25)

- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [A Transformer-based Framework for POI-level Social Post Geolocation](https://mlsanity.com/paper/2211.01336/) (2022)
- [Designing Robust Transformers using Robust Kernel Density Estimation](https://mlsanity.com/paper/2210.05794/) (2022)
- [Elliptical Attention](https://mlsanity.com/paper/2406.13770/) (2024)
- [Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning](https://mlsanity.com/paper/2108.00356/) (2021)
- [Transformer-based Multi-task Learning for Disaster Tweet Categorisation](https://mlsanity.com/paper/2110.08010/) (2021)
- [ProTransformer: Robustify Transformers via Plug-and-Play Paradigm](https://mlsanity.com/paper/2410.23182/) (2024)
- [Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers](https://mlsanity.com/paper/2204.10230/) (2022)
- [Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact](https://mlsanity.com/paper/2410.17532/) (2024)
- [Multi-task transfer learning for finding actionable information from crisis-related messages on social media](https://mlsanity.com/paper/2102.13395/) (2021)
- [Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction](https://mlsanity.com/paper/2403.13872/) (2024)
- [Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data](https://mlsanity.com/paper/2106.07114/) (2021)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling](https://mlsanity.com/paper/2112.12940/) (2021)
- [Contrastive Learning of Sociopragmatic Meaning in Social Media](https://mlsanity.com/paper/2203.07648/) (2022)
- [Optimizing Group Utility in Itinerary Planning: A Strategic and Crowd-Aware Approach](https://mlsanity.com/paper/2304.08495/) (2023)
- [HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid](https://mlsanity.com/paper/2410.06370/) (2024)
- [Crisis Domain Adaptation Using Sequence-to-sequence Transformers](https://mlsanity.com/paper/2110.08015/) (2021)
- [Detection of Dangerous Events on Social Media: A Perspective Review](https://mlsanity.com/paper/2204.01351/) (2022)
- [The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning](https://mlsanity.com/paper/2210.03352/) (2022)
- [Multimodal Learning with Augmentation Techniques for Natural Disaster Assessment](https://mlsanity.com/paper/2511.00004/) (2025)
- [Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models](https://mlsanity.com/paper/2511.21753/) (2025)
- [Cross-Modal Rationale Transfer for Explainable Humanitarian Classification on Social Media](https://mlsanity.com/paper/2603.18611/) (2026)
- [Guide Me Out: A Framework to Benchmark VLM Operators Communication in Crisis Scenarios](https://mlsanity.com/paper/2606.09428/) (2026)
- [CrisisFake: Benchmark Validity of AI-Generated Text Detection for Disaster Social Sensing](https://mlsanity.com/paper/2609.35821/) (2026)

## Abstract (arXiv)

Classification of crisis events, such as natural disasters, terrorist attacks and pandemics, is a crucial task to create early signals and inform relevant parties for spontaneous actions to reduce overall damage. Despite crisis such as natural disasters can be predicted by professional institutions, certain events are first signaled by civilians, such as the recent COVID-19 pandemics. Social media platforms such as Twitter often exposes firsthand signals on such crises through high volume information exchange over half a billion tweets posted daily. Prior works proposed various crisis embeddings and classification using conventional Machine Learning and Neural Network models. However, none of the works perform crisis embedding and classification using state of the art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores. The proposed model also demonstrates superior robustness over benchmark, as it shows marginal performance compromise while extending from 6 to 36 events with only 51.4% additional data points. We also proposed Crisis2Vec, an attention-based, document-level contextual embedding architecture for crisis embedding, which achieve better performance than conventional crisis embedding methods such as Word2Vec and GloVe. To the best of our knowledge, our works are first to propose using transformer-based crisis classification and document-level contextual crisis embedding in the literature.