Abstract
Event detection is a concept that is crucial to the assurance of public safety surrounding real-world events. Decision makers use information from a range of terrestrial and online sources to help inform decisions that enable them to develop policies and react appropriately to events as they unfold. One such source of online information is social media. Twitter, as a form of social media, is a popular micro-blogging web application serving hundreds of millions of users. User-generated content can be utilized as a rich source of information to identify real-world events. In this paper, we present a novel detection framework for identifying such events, with a focus on 'disruptive' events using Twitter data. The approach is based on five steps; data collection, preprocessing, classification, clustering and summarization. We use a Naive Bayes classification model and an Online Clustering method to validate our model over multiple real-world data sets. To the best of our knowledge, this study is the first effort to identify real-world events in Arabic from social media.