Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.01262 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912740436606976 |
|---|---|
| author | Wu, Junfeng Zhou, Xiangmin Kuligowski, Erica Singh, Dhirendra Ronchi, Enrico Kinateder, Max |
| author_facet | Wu, Junfeng Zhou, Xiangmin Kuligowski, Erica Singh, Dhirendra Ronchi, Enrico Kinateder, Max |
| contents | 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, personalised 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Social Media Data Mining of Human Behaviour during Bushfire Evacuation Wu, Junfeng Zhou, Xiangmin Kuligowski, Erica Singh, Dhirendra Ronchi, Enrico Kinateder, Max Social and Information Networks Artificial Intelligence Emerging Technologies Machine Learning 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, personalised 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. |
| title | Social Media Data Mining of Human Behaviour during Bushfire Evacuation |
| topic | Social and Information Networks Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2512.01262 |