# A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria

arXiv: https://arxiv.org/abs/1805.05144 · HTML: https://mlsanity.com/paper/1805.05144/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 12 of 12)

- [CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://mlsanity.com/paper/2309.05494/) (2023)
- [Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features](https://mlsanity.com/paper/2412.10413/) (2024)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision](https://mlsanity.com/paper/2207.04947/) (2022)
- [Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://mlsanity.com/paper/2403.16614/) (2024)
- [Identifying epidemic related Tweets using noisy learning](https://mlsanity.com/paper/2209.12614/) (2022)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing](https://mlsanity.com/paper/2111.04052/) (2021)
- [Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster](https://mlsanity.com/paper/2302.13403/) (2023)
- [Tracking electricity losses and their perceived causes using nighttime light and social media](https://mlsanity.com/paper/2310.12346/) (2023)
- [SocialED: A Python Library for Social Event Detection](https://mlsanity.com/paper/2412.13472/) (2024)
- [Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models](https://mlsanity.com/paper/2511.21753/) (2025)

## Abstract (arXiv)

People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.