  Media Storms, dramatic outbursts of attention to a story, are central
components of media dynamics and the attention landscape. Despite their
significance, there has been little systematic and empirical research on this
concept due to issues of measurement and operationalization. We introduce an
iterative human-in-the-loop method to identify media storms in a large-scale
corpus of news articles. The text is first transformed into signals of
dispersion based on several textual characteristics. In each iteration, we
apply unsupervised anomaly detection to these signals; each anomaly is then
validated by an expert to confirm the presence of a storm, and those results
are then used to tune the anomaly detection in the next iteration. We
demonstrate the applicability of this method in two scenarios: first,
supplementing an initial list of media storms within a specific time frame; and
second, detecting media storms in new time periods. We make available a media
storm dataset compiled using both scenarios. Both the method and dataset offer
the basis for comprehensive empirical research into the concept of media
storms, including characterizing them and predicting their outbursts and
durations, in mainstream media or social media platforms.
