# A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma

arXiv: https://arxiv.org/abs/2101.00480 · HTML: https://mlsanity.com/paper/2101.00480/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 82 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://mlsanity.com/paper/1601.03764/) (2016)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages](https://mlsanity.com/paper/1605.05894/) (2016)
- [A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/1812.00315/) (2018)
- [Automatically Identifying Fake News in Popular Twitter Threads](https://mlsanity.com/paper/1705.01613/) (2017)
- [The DARPA Twitter Bot Challenge](https://mlsanity.com/paper/1601.05140/) (2016)
- [Twitter Speaks: A Case of National Disaster Situational Awareness](https://mlsanity.com/paper/1903.02706/) (2019)
- [Polarization and Fake News: Early Warning of Potential Misinformation Targets](https://mlsanity.com/paper/1802.01400/) (2018)
- [Localizing and Quantifying Damage in Social Media Images](https://mlsanity.com/paper/1806.07378/) (2018)
- [Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence](https://mlsanity.com/paper/2004.06675/) (2020)
- [Forecasting People's Needs in Hurricane Events from Social Network](https://mlsanity.com/paper/1811.04577/) (2018)
- [Social media usage patterns during natural hazards](https://mlsanity.com/paper/1806.07451/) (2018)

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

- [Disaster Management in the Era of Agentic AI Systems: A Vision for Collective Human-Machine Intelligence for Augmented Resilience](https://mlsanity.com/paper/2510.16034/) (2025)
- [Information Retrieval and Classification of Real-Time Multi-Source Hurricane Evacuation Notices](https://mlsanity.com/paper/2401.06789/) (2024)

## Abstract (arXiv)

Streaming social media provides a real-time glimpse of extreme weather impacts. However, the volume of streaming data makes mining information a challenge for emergency managers, policy makers, and disciplinary scientists. Here we explore the effectiveness of data learned approaches to mine and filter information from streaming social media data from Hurricane Irma's landfall in Florida, USA. We use 54,383 Twitter messages (out of 784K geolocated messages) from 16,598 users from Sept. 10 - 12, 2017 to develop 4 independent models to filter data for relevance: 1) a geospatial model based on forcing conditions at the place and time of each tweet, 2) an image classification model for tweets that include images, 3) a user model to predict the reliability of the tweeter, and 4) a text model to determine if the text is related to Hurricane Irma. All four models are independently tested, and can be combined to quickly filter and visualize tweets based on user-defined thresholds for each submodel. We envision that this type of filtering and visualization routine can be useful as a base model for data capture from noisy sources such as Twitter. The data can then be subsequently used by policy makers, environmental managers, emergency managers, and domain scientists interested in finding tweets with specific attributes to use during different stages of the disaster (e.g., preparedness, response, and recovery), or for detailed research.