# RweetMiner: Automatic identification and categorization of help requests on twitter during disasters

arXiv: https://arxiv.org/abs/2303.02399 · HTML: https://mlsanity.com/paper/2303.02399/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 51 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [Processing Social Media Messages in Mass Emergency: A Survey](https://mlsanity.com/paper/1407.7071/) (2014)
- [Through the Twitter Glass: Detecting Questions in Micro-Text](https://mlsanity.com/paper/2006.07732/) (2020)

## Built on by (most cited first, 5 of 5)

- [CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media](https://mlsanity.com/paper/2405.11897/) (2024)
- [Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs](https://mlsanity.com/paper/2504.16144/) (2025)
- [ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization](https://mlsanity.com/paper/2405.06551/) (2024)
- [Flood of Techniques and Drought of Theories: Emotion Mining in Disasters](https://mlsanity.com/paper/2407.05219/) (2024)
- ["Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts](https://mlsanity.com/paper/2502.16839/) (2025)

## Abstract (arXiv)

Catastrophic events create uncertain situations for humanitarian organizations locating and providing aid to affected people. Many people turn to social media during disasters for requesting help and/or providing relief to others. However, the majority of social media posts seeking help could not properly be detected and remained concealed because often they are noisy and ill-formed. Existing systems lack in planning an effective strategy for tweet preprocessing and grasping the contexts of tweets. This research, first of all, formally defines request tweets in the context of social networking sites, hereafter rweets, along with their different primary types and sub-types. Our main contributions are the identification and categorization of rweets. For rweet identification, we employ two approaches, namely a rule-based and logistic regression, and show their high precision and F1 scores. The rweets classification into sub-types such as medical, food, and shelter, using logistic regression shows promising results and outperforms existing works. Finally, we introduce an architecture to store intermediate data to accelerate the development process of the machine learning classifiers.