Smart City Gnosys
Smart city article details
| Title | Investigating Disaster Response For Resilient Communities Through Social Media Data And The Susceptible-Infected-Recovered (Sir) Model: A Case Study Of 2020 Western U.S. Wildfire Season |
|---|---|
| ID_Doc | 33375 |
| Authors | Ma Z.; Li L.; Hemphill L.; Baecher G.B.; Yuan Y. |
| Year | 2024 |
| Published | Sustainable Cities and Society, 106 |
| DOI | http://dx.doi.org/10.1016/j.scs.2024.105362 |
| Abstract | Effective disaster response is critical for communities to remain resilient and advance the development of smart cities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and behaviors during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics: “health impact,” “damage,” and “evacuation.” We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study offers a quantitative approach to measure disaster response and support community resilience enhancement. © 2024 |
| Author Keywords | BERT topic modeling; Community resilience; SIR model; Social media; Wildfire response |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
10132  | 0.892 | Samuels R.; Taylor J.E. | Applied Methodology For Identifying Hurricane-Induced Social Media Signal Changes In Vulnerable Populations | Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019) |
| 37366 | 0.874 | Kar B.; Ethridge J. | Mobility Pattern Analysis For Power Restoration Activities Using Geo-Tagged Tweets | Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 (2019) |
| 52030 | 0.866 | Khatoon S.; Asif A.; Hasan M.M.; Alshamari M. | Social Media-Based Intelligence For Disaster Response And Management In Smart Cities | Springer Optimization and Its Applications, 186 (2022) |
| 3082 | 0.862 | Burak Can A.; Burak Parlak I.; Acarman T. | A New Method Of Automatic Content Analysis In Disaster Management | 10th International Symposium on Digital Forensics and Security, ISDFS 2022 (2022) |
| 12859 | 0.85 | Kangana N.; Kankanamge N.; De Silva C.; Goonetilleke A.; Mahamood R.; Ranasinghe D. | Bridging Community Engagement And Technological Innovation For Creating Smart And Resilient Cities: A Systematic Literature Review | Smart Cities, 7, 6 (2024) |