Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment

@inproceedings{Resch2017CombiningMT,
  title={Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment},
  author={Bernd Resch and Florian Usl{\"a}nder and Clemens Havas},
  year={2017}
}
Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machinelearning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis… CONTINUE READING
8 Citations
47 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-8 of 8 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 47 references

December). Applications of topics models to analysis of disaster-related Twitter data

  • K. Kireyev, L. Palen, K. Anderson
  • NIPS Workshop on Applications for Topic Models…
  • 2009
Highly Influential
5 Excerpts

LandScan Global Population Database

  • Oak Ridge National Laboratory.
  • Retrieved from: http://web.ornl. gov/sci/landscan…
  • 2017

Earthquakes

  • US Geological Survey.
  • Retrieved from http://earthquake.usgs.gov…
  • 2016

Similar Papers

Loading similar papers…