Social media data mining of human behaviour during bushfire evacuation
Wu, Junfeng ; Zhou, Xiangmin ; Kuligowski, Erica ; Singh, Dhirendra ; Ronchi, Enrico LU
and Kinateder, Max
(2026)
In International Journal of Data Science and Analytics
21(1).
- Abstract
Traditional data sources on bushfire evacuation behaviour, such as quantitative surveys and manual observations have severe limitations. Mining social media data related to bushfire evacuations promises to close this gap by allowing the collection and processing of a large amount of behavioural data, which are low-cost, accurate, possibly including location information and rich contextual information. However, social media data have many limitations, such as being scattered, incomplete, informal, etc. Together, these limitations represent several challenges to their usefulness to better understand bushfire evacuation. To overcome these challenges and provide guidance on which and how social media data can be used, this scoping review of... (More)
Traditional data sources on bushfire evacuation behaviour, such as quantitative surveys and manual observations have severe limitations. Mining social media data related to bushfire evacuations promises to close this gap by allowing the collection and processing of a large amount of behavioural data, which are low-cost, accurate, possibly including location information and rich contextual information. However, social media data have many limitations, such as being scattered, incomplete, informal, etc. Together, these limitations represent several challenges to their usefulness to better understand bushfire evacuation. To overcome these challenges and provide guidance on which and how social media data can be used, this scoping review of the literature reports on recent advances in relevant data mining techniques. In addition, future applications and open problems are discussed. We envision future applications such as evacuation model calibration and validation, emergency communication, personalized evacuation training, and resource allocation for evacuation preparedness. We identify open problems such as data quality, bias and representativeness, geolocation accuracy, contextual understanding, crisis-specific lexicon and semantics, and multimodal data interpretation.
(Less)
- author
- Wu, Junfeng
; Zhou, Xiangmin
; Kuligowski, Erica
; Singh, Dhirendra
; Ronchi, Enrico
LU
and Kinateder, Max
- organization
- publishing date
- 2026-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bushfire, Data mining, Evacuation, Social media, Wildfire
- in
- International Journal of Data Science and Analytics
- volume
- 21
- issue
- 1
- article number
- 14
- external identifiers
-
- scopus:105023390974
- ISSN
- 2364-415X
- DOI
- 10.1007/s41060-025-00884-y
- language
- English
- LU publication?
- yes
- id
- 09b984ba-04d1-4aeb-a54d-238d938610ec
- date added to LUP
- 2026-02-10 15:05:46
- date last changed
- 2026-02-10 15:07:04
@article{09b984ba-04d1-4aeb-a54d-238d938610ec,
abstract = {{<p>Traditional data sources on bushfire evacuation behaviour, such as quantitative surveys and manual observations have severe limitations. Mining social media data related to bushfire evacuations promises to close this gap by allowing the collection and processing of a large amount of behavioural data, which are low-cost, accurate, possibly including location information and rich contextual information. However, social media data have many limitations, such as being scattered, incomplete, informal, etc. Together, these limitations represent several challenges to their usefulness to better understand bushfire evacuation. To overcome these challenges and provide guidance on which and how social media data can be used, this scoping review of the literature reports on recent advances in relevant data mining techniques. In addition, future applications and open problems are discussed. We envision future applications such as evacuation model calibration and validation, emergency communication, personalized evacuation training, and resource allocation for evacuation preparedness. We identify open problems such as data quality, bias and representativeness, geolocation accuracy, contextual understanding, crisis-specific lexicon and semantics, and multimodal data interpretation.</p>}},
author = {{Wu, Junfeng and Zhou, Xiangmin and Kuligowski, Erica and Singh, Dhirendra and Ronchi, Enrico and Kinateder, Max}},
issn = {{2364-415X}},
keywords = {{Bushfire; Data mining; Evacuation; Social media; Wildfire}},
language = {{eng}},
number = {{1}},
series = {{International Journal of Data Science and Analytics}},
title = {{Social media data mining of human behaviour during bushfire evacuation}},
url = {{http://dx.doi.org/10.1007/s41060-025-00884-y}},
doi = {{10.1007/s41060-025-00884-y}},
volume = {{21}},
year = {{2026}},
}