%0 Journal Article %1 kibanov2017mining %A Kibanov, Mark %A Stumme, Gerd %A Amin, Imaduddin %A Lee, Jong Gun %D 2017 %J Social Network Analysis and Mining %K 2017 myown twitter %N 1 %P 30 %R 10.1007/s13278-017-0446-1 %T Mining social media to inform peatland fire and haze disaster management %U http://rdcu.be/udmt %V 7 %X 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 analyze temporal correlations between the datasets and their geo-spatial interdependence. Furthermore, we show how Twitter data reveal changes in users' behavior during severe haze events. Overall, we demonstrate that social media are 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.

@article{kibanov2017mining, 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 analyze temporal correlations between the datasets and their geo-spatial interdependence. Furthermore, we show how Twitter data reveal changes in users' behavior during severe haze events. Overall, we demonstrate that social media are 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.}, added-at = {2017-07-19T08:16:13.000+0200}, author = {Kibanov, Mark and Stumme, Gerd and Amin, Imaduddin and Lee, Jong Gun}, biburl = {https://www.bibsonomy.org/bibtex/26b97a4f339b2f986059a99f6d0c350d4/kibanov}, day = 14, description = {Mining social media to inform peatland fire and haze disaster management | SpringerLink}, doi = {10.1007/s13278-017-0446-1}, interhash = {0709bd322cb1bc18fc9a04ac775eecac}, intrahash = {6b97a4f339b2f986059a99f6d0c350d4}, issn = {1869-5469}, journal = {Social Network Analysis and Mining}, keywords = {2017 myown twitter}, month = jul, number = 1, pages = 30, timestamp = {2017-07-19T08:16:13.000+0200}, title = {Mining social media to inform peatland fire and haze disaster management}, url = {http://rdcu.be/udmt}, volume = 7, year = 2017 }