@inproceedings{litterer-etal-2023-rains,
title = "When it Rains, it Pours: Modeling Media Storms and the News Ecosystem",
author = "Litterer, Benjamin and
Jurgens, David and
Card, Dallas",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.420",
doi = "10.18653/v1/2023.findings-emnlp.420",
pages = "6346--6361",
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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%0 Conference Proceedings
%T When it Rains, it Pours: Modeling Media Storms and the News Ecosystem
%A Litterer, Benjamin
%A Jurgens, David
%A Card, Dallas
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F litterer-etal-2023-rains
%X 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.
%R 10.18653/v1/2023.findings-emnlp.420
%U https://aclanthology.org/2023.findings-emnlp.420
%U https://doi.org/10.18653/v1/2023.findings-emnlp.420
%P 6346-6361
Markdown (Informal)
[When it Rains, it Pours: Modeling Media Storms and the News Ecosystem](https://aclanthology.org/2023.findings-emnlp.420) (Litterer et al., Findings 2023)
ACL