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Users increasingly inform themselves of the latest news through online news services. This is further accentuated by the increasingly seamless integration of social network platforms such as Twitter and Facebook into news websites, allowing easy content sharing and distribution. This makes online social network platforms of particular interest to news providers. For instance, online news producers use Twitter to disseminate articles published on their website, to assess the popularity of their contents, but also as an information source to be used on itself. In this paper, we focus on Twitter as a medium to help journalists and news editors rapidly detect follow-up stories to the articles they publish. We propose to do so by leveraging “transient news crowds”, which are loosely-coupled groups that appear in Twitter around a particular news item, and where transient here reflects the fleeting nature of news. We define transient news crowds on Twitter, study their characteristics, and investigate how their characteristics can be used to discover related news. We validate our approach using Twitter data around news stories published by the BBC and Al Jazeera.
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Stored only when both papers are in the corpus — 24 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.