Twitter Speaks: A Case of National Disaster Situational Awareness
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2019.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
9 papers.
- 2021A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Featurescited by 12
- 2024Can LLMs Learn Macroeconomic Narratives from Social Media?cited by 7
- 2021Efficacy of BERT embeddings on predicting disaster from Twitter datacited by 5
- 2022Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Surveycited by 5
- 2022Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformerscited by 3
- 2021A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irmacited by 2
- 2026Assessing socio-economic climate impacts from text datacited by 1
- 2022Twitter Data Analysis: Izmir Earthquake Casecited by 0
- 2023Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire seasoncited by 0
- 2026Assessing socio-economic climate impacts from text data20 May 2026
- 2024Can LLMs Learn Macroeconomic Narratives from Social Media?17 Jun 2024
- 2023Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season10 Aug 2023
- 2022Twitter Data Analysis: Izmir Earthquake Case2 Dec 2022
- 2022Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey13 Sep 2022
- 2022Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers21 Apr 2022
- 2021A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features9 Nov 2021
- 2021Efficacy of BERT embeddings on predicting disaster from Twitter data8 Aug 2021
- 2021A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma2 Jan 2021