# I-AID: Identifying Actionable Information from Disaster-related Tweets

arXiv: https://arxiv.org/abs/2008.13544 · HTML: https://mlsanity.com/paper/2008.13544/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification](https://mlsanity.com/paper/2203.02912/) (2022)
- [Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning](https://mlsanity.com/paper/2211.11468/) (2022)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning](https://mlsanity.com/paper/2210.03352/) (2022)
- [Information Type Classification with Contrastive Task-Specialized Sentence Encoders](https://mlsanity.com/paper/2312.11020/) (2023)

## Abstract (arXiv)

Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. Recent studies highlight the need to filter information on social media into fine-grained content labels. However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. In this paper, we propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types and filter critical information from the enormous volume of social media data. I-AID incorporates three main components: i) a BERT-based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets' words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding information types in a supervised way. We conducted several experiments on two real publicly-available datasets. Our results indicate that I-AID outperforms state-of-the-art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.