Practical extraction of disaster-relevant information from social media

Authors:
Muhammad Imran;Shady Elbassuoni;Carlos Castillo;Fernando Diaz;Patrick Meier
Affiliations:
University of Trento, Trento, Italy;American University of Beirut, Beirut, Lebanon;Qatar Computing Research Institute, Doha, Qatar;Microsoft Research, New York, USA;Qatar Computing Research Institute, Doha, Qatar
Venue:
Proceedings of the 22nd international conference on World Wide Web companion
Year:
2013

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Abstract

During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.