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

TL;DR

This paper introduces a pairwise article similarity model to identify and analyze media storms in online news, revealing patterns in their evolution, topical distribution, and influence on media coverage over two years.

Contribution

It presents a novel method for detecting media storms through article similarity, enabling detailed analysis of their dynamics and influence in the news ecosystem.

Findings

Media storms can be systematically identified and analyzed.

Storm evolution follows predictable patterns.

Media storms influence intermedia agenda setting.

Abstract

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.