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Most events in the world receive at most brief coverage by the news media.Occasionally, however, an event will trigger a media storm, with voluminous andwidespread coverage lasting for weeks instead of days. In this work, we developand apply a pairwise article similarity model, allowing us to identify storyclusters in corpora covering local and national online news, and thereby createa comprehensive corpus of media storms over a nearly two year period. Usingthis corpus, we investigate media storms at a new level of granularity,allowing us to validate claims about storm evolution and topical distribution,and provide empirical support for previously hypothesized patterns of influenceof storms on media coverage and intermedia agenda setting.

When it Rains, it Pours: Modeling Media Storms and the News Ecosystem