# Social Media Data Mining of Human Behaviour during Bushfire Evacuation

arXiv: https://arxiv.org/abs/2512.01262 · HTML: https://mlsanity.com/paper/2512.01262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 201 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages](https://mlsanity.com/paper/1605.05894/) (2016)
- [Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks](https://mlsanity.com/paper/2104.00853/) (2021)
- [Detecting natural disasters, damage, and incidents in the wild](https://mlsanity.com/paper/2008.09188/) (2020)
- [Visual Sentiment Analysis from Disaster Images in Social Media](https://mlsanity.com/paper/2009.03051/) (2020)
- [Sentiment Analysis from Images of Natural Disasters](https://mlsanity.com/paper/1910.04416/) (2019)
- [On Identifying Hashtags in Disaster Twitter Data](https://mlsanity.com/paper/2001.01323/) (2020)
- [Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data](https://mlsanity.com/paper/2109.07745/) (2021)
- [The amplification of risk in experimental diffusion chains](https://mlsanity.com/paper/1504.05331/) (2015)
- [Identifying Relevant Messages in a Twitter-based Citizen Channel for Natural Disaster Situations](https://mlsanity.com/paper/1503.05784/) (2015)
- [Spatial Data Mining of Public Transport Incidents reported in Social Media](https://mlsanity.com/paper/2110.05573/) (2021)

## Abstract (arXiv)

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.