# Twitter Speaks: A Case of National Disaster Situational Awareness

arXiv: https://arxiv.org/abs/1903.02706 · HTML: https://mlsanity.com/paper/1903.02706/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 9 of 9)

- [A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features](https://mlsanity.com/paper/2111.08450/) (2021)
- [Can LLMs Learn Macroeconomic Narratives from Social Media?](https://mlsanity.com/paper/2406.12109/) (2024)
- [Efficacy of BERT embeddings on predicting disaster from Twitter data](https://mlsanity.com/paper/2108.10698/) (2021)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers](https://mlsanity.com/paper/2204.10230/) (2022)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [Assessing socio-economic climate impacts from text data](https://mlsanity.com/paper/2605.20793/) (2026)
- [Twitter Data Analysis: Izmir Earthquake Case](https://mlsanity.com/paper/2212.01453/) (2022)
- [Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season](https://mlsanity.com/paper/2308.05281/) (2023)

## Abstract (arXiv)

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyze public concerns during natural disasters; however, this approach is limited, expensive, and time-consuming. Luckily the advent of social media has provided scholars with an alternative means of analyzing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasizes the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modeling to create a better SA for disaster preparedness, response, and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.