Practical extraction of disaster-relevant information from social media

Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz, Patrick Meier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

114 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationWWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web
Pages1021-1024
Number of pages4
Publication statusPublished - 1 Dec 2013
Event22nd International Conference on World Wide Web, WWW 2013 - Rio de Janeiro, Brazil
Duration: 13 May 201317 May 2013

Publication series

NameWWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web

Other

Other22nd International Conference on World Wide Web, WWW 2013
CountryBrazil
CityRio de Janeiro
Period13/5/1317/5/13

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Keywords

  • Information extraction
  • Information filtering
  • Social media

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Imran, M., Elbassuoni, S., Castillo, C., Diaz, F., & Meier, P. (2013). Practical extraction of disaster-relevant information from social media. In WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web (pp. 1021-1024). (WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web).