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

arXiv: https://arxiv.org/abs/2312.02118 · HTML: https://mlsanity.com/paper/2312.02118/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/2102.04567/) (2021)
- [NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystems](https://mlsanity.com/paper/2203.08600/) (2022)
- [HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity](https://mlsanity.com/paper/2204.04844/) (2022)

## Built on by (most cited first, 2 of 2)

- [Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus](https://mlsanity.com/paper/2411.07892/) (2024)
- [Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora](https://mlsanity.com/paper/2404.09299/) (2024)

## Abstract (arXiv)

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.