“Breaking” disasters: Predicting and characterizing the global news value of natural and man-made disasters
published: Dec. 1, 2017, recorded: August 2017, views: 790
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Due to their oen unexpected nature, natural and man-made disasters are dicult 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. Twier, 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 oen follow these sources via Twier’s lists, but have to comb through thousands of small-scale or low-impact stories to nd events that may be globally relevant. ese 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. e results are promising and can be used in a predictive seing to help journalists manage their sources more eectively, or in a descriptive manner to analyze media coverage of disasters. rough 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 Twier, which we have made publicly available.
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