Mining Social Media to Inform Peatland Fire and Haze Disaster Management
June 16, 2017 Β· Declared Dead Β· π Social Network Analysis and Mining
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mark Kibanov, Gerd Stumme, Imaduddin Amin, Jong Gun Lee
arXiv ID
1706.05406
Category
cs.SI: Social & Info Networks
Citations
51
Venue
Social Network Analysis and Mining
Last Checked
5 months ago
Abstract
Peatland fires and haze events are disasters with national, regional and international implications. The phenomena lead to direct damage to local assets, as well as broader economic and environmental losses. Satellite imagery is still the main and often the only available source of information for disaster management. In this article, we test the potential of social media to assist disaster management. To this end, we compare insights from two datasets: fire hotspots detected via NASA satellite imagery and almost all GPS-stamped tweets from Sumatra Island, Indonesia, posted during 2014. Sumatra Island is chosen as it regularly experiences a significant number of haze events, which affect citizens in Indonesia as well as in nearby countries including Malaysia and Singapore. We analyse temporal correlations between the datasets and their geo-spatial interdependence. Furthermore, we show how Twitter data reveals changes in users' behavior during severe haze events. Overall, we demonstrate that social media is a valuable source of complementary and supplementary information for haze disaster management. Based on our methodology and findings, an analytics tool to improve peatland fire and haze disaster management by the Indonesian authorities is under development.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Social & Info Networks
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
π»
Ghosted
Natural Scales in Geographical Patterns
R.I.P.
π»
Ghosted
Representation Learning on Graphs: Methods and Applications
R.I.P.
π»
Ghosted
The COVID-19 Social Media Infodemic
R.I.P.
π»
Ghosted
OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted