# TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision

arXiv: https://arxiv.org/abs/2207.04947 · HTML: https://mlsanity.com/paper/2207.04947/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages](https://mlsanity.com/paper/1605.05894/) (2016)
- [A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria](https://mlsanity.com/paper/1805.05144/) (2018)
- [Social Media Mining Toolkit (SMMT)](https://mlsanity.com/paper/2003.13894/) (2020)
- [Tweet, but Verify: Epistemic Study of Information Verification on Twitter](https://mlsanity.com/paper/1312.5297/) (2013)

## Built on by (most cited first, 2 of 2)

- [Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets](https://mlsanity.com/paper/2309.06503/) (2023)
- [Utilizing Weak Supervision To Generate Indonesian Conservation Dataset](https://mlsanity.com/paper/2310.11258/) (2023)

## Abstract (arXiv)

Social media is often utilized as a lifeline for communication during natural disasters. Traditionally, natural disaster tweets are filtered from the Twitter stream using the name of the natural disaster and the filtered tweets are sent for human annotation. The process of human annotation to create labeled sets for machine learning models is laborious, time consuming, at times inaccurate, and more importantly not scalable in terms of size and real-time use. In this work, we curate a silver standard dataset using weak supervision. In order to validate its utility, we train machine learning models on the weakly supervised data to identify three different types of natural disasters i.e earthquakes, hurricanes and floods. Our results demonstrate that models trained on the silver standard dataset achieved performance greater than 90% when classifying a manually curated, gold-standard dataset. To enable reproducible research and additional downstream utility, we release the silver standard dataset for the scientific community.