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

Benjamin Litterer, David Jurgens, Dallas Card
12/4/2023
cs.CLcs.CYcs.SI

No official code found. Best implementation: blitt2018/mediaStorms (4 stars, last commit 2023-12-26).

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.

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Code Implementations(1)

blitt2018/mediaStormsCommunity100%

local news project

40HTML, PerlOct 24, 20222 years agoNOASSERTION

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Cite this paper

@article{litterer2023when,
  title  = {When it Rains, it Pours: Modeling Media Storms and the News Ecosystem},
  author = {Benjamin Litterer and David Jurgens and Dallas Card},
  year   = {2023},
  eprint = {2312.02118},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.02118v1}
}

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