  Due to their often unexpected nature, natural and man-made disasters are
difficult to monitor and detect for journalists and disaster management
response teams. Journalists are increasingly relying on signals from social
media 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 early
signal for disaster detection. Journalists who work for global desks often
follow these sources via Twitter's lists, but have to comb through thousands of
small-scale or low-impact stories to find events that may be globally relevant.
These are events that have a large scope, high impact, or potential
geo-political relevance. We propose a model for automatically identifying
events from local news sources that may break on a global scale within the next
24 hours. The results are promising and can be used in a predictive setting to
help journalists manage their sources more effectively, or in a descriptive
manner to analyze media coverage of disasters. Through the feature evaluation
process, we also address the question: "what makes a disaster event newsworthy
on a global scale?" As part of our data collection process, we have created a
list of local sources of disaster/accident news on Twitter, which we have made
publicly available.
