While categorizing any type of user-generated content online is a challenging problem, categorizing social media messages during a crisis situation adds an additional layer of complexity, due to the volume and variability of information, and to the fact that these messages must be classified as soon as they arrive. Current approaches involve the use of automatic classification, human classification, or a mixture of both. In these types of approaches, there are several reasons to keep the number of information categories small and updated, which we examine in this article. This means at the onset of a crisis an expert must select a handful of information categories into which information will be categorized. The next step, as the crisis unfolds, is to dynamically change the initial set as new information is posted online. In this paper, we propose an effective way to dynamically extract emerging, potentially interesting, new categories from social media data.
Imran, Muhammad and Carlos Castillo. 2015. "Towards a Data-driven Approach to Identify Crisis-Related Topics in Social Media Streams." Presented at the International World Wide Web Conference, Florence. http://dl.acm.org/citation.cfm?id=2741729