Practical extraction of disaster-relevant information from social media

• DOI: 10.1145/2487788.2488109
• Publication Date: 2016-02-05T19:50:40Z
• Authors: Muhammad Imran; Shady Elbassuoni; Carlos Castillo; Fernando Diaz; Patrick Meier
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

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.