# Detecting Perceived Emotions in Hurricane Disasters

arXiv: https://arxiv.org/abs/2004.14299 · HTML: https://mlsanity.com/paper/2004.14299/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Whose Emotions and Moral Sentiments Do Language Models Reflect?](https://mlsanity.com/paper/2402.11114/) (2024)
- [Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models](https://mlsanity.com/paper/2310.14389/) (2023)
- [How people talk about each other: Modeling Generalized Intergroup Bias and Emotion](https://mlsanity.com/paper/2209.06687/) (2022)
- [Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts](https://mlsanity.com/paper/2210.12531/) (2022)
- [Unsupervised Extractive Summarization of Emotion Triggers](https://mlsanity.com/paper/2306.01444/) (2023)
- [Emotion analysis and detection during COVID-19](https://mlsanity.com/paper/2107.11020/) (2021)
- [Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response](https://mlsanity.com/paper/2402.13528/) (2024)
- [Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion](https://mlsanity.com/paper/2311.09602/) (2023)
- [Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias](https://mlsanity.com/paper/2305.16409/) (2023)
- [Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance](https://mlsanity.com/paper/2312.14943/) (2023)
- [Analysing the Greek Parliament Records with Emotion Classification](https://mlsanity.com/paper/2205.12012/) (2022)
- [Incorporating Emotions into Health Mention Classification Task on Social Media](https://mlsanity.com/paper/2212.05039/) (2022)
- [Multi-task Learning for Personal Health Mention Detection on Social Media](https://mlsanity.com/paper/2212.05147/) (2022)
- [How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications](https://mlsanity.com/paper/2601.17020/) (2026)

## Abstract (arXiv)

Natural disasters (e.g., hurricanes) affect millions of people each year, causing widespread destruction in their wake. People have recently taken to social media websites (e.g., Twitter) to share their sentiments and feelings with the larger community. Consequently, these platforms have become instrumental in understanding and perceiving emotions at scale. In this paper, we introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria. We present a comprehensive study of fine-grained emotions and propose classification tasks to discriminate between coarse-grained emotion groups. Our best BERT model, even after task-guided pre-training which leverages unlabeled Twitter data, achieves only 68% accuracy (averaged across all groups). HurricaneEmo serves not only as a challenging benchmark for models but also as a valuable resource for analyzing emotions in disaster-centric domains.