@inproceedings{67250a3edbdd424ea912ef2c6b35e5e6,
title = "Practical extraction of disaster-relevant information from social media",
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.",
keywords = "Information extraction, Information filtering, Social media",
author = "Muhammad Imran and Shady Elbassuoni and Carlos Castillo and Fernando Diaz and Patrick Meier",
year = "2013",
doi = "10.1145/2487788.2488109",
language = "English",
isbn = "9781450320382",
series = "WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web",
publisher = "Association for Computing Machinery",
pages = "1021--1024",
booktitle = "WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web",
address = "United States",
note = "WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web ; Conference date: 13-05-2013 Through 17-05-2013",
}