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Due to their often unexpected nature, natural and man-made disasters aredifficult to monitor and detect for journalists and disaster managementresponse teams. Journalists are increasingly relying on signals from socialmedia to detect such stories in their early stage of development. Twitter,which features a vast network of local news outlets, is a major source of earlysignal for disaster detection. Journalists who work for global desks oftenfollow these sources via Twitter's lists, but have to comb through thousands ofsmall-scale or low-impact stories to find events that may be globally relevant.These are events that have a large scope, high impact, or potentialgeo-political relevance. We propose a model for automatically identifyingevents from local news sources that may break on a global scale within the next24 hours. The results are promising and can be used in a predictive setting tohelp journalists manage their sources more effectively, or in a descriptivemanner to analyze media coverage of disasters. Through the feature evaluationprocess, we also address the question: "what makes a disaster event newsworthyon a global scale?" As part of our data collection process, we have created alist of local sources of disaster/accident news on Twitter, which we have madepublicly available.

"Breaking" Disasters: Predicting and Characterizing the Global News Value of Natural and Man-made Disasters