  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 and
widespread coverage lasting for weeks instead of days. In this work, we develop
and apply a pairwise article similarity model, allowing us to identify story
clusters in corpora covering local and national online news, and thereby create
a comprehensive corpus of media storms over a nearly two year period. Using
this 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 influence
of storms on media coverage and intermedia agenda setting.
