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In the present global media climate, speed and immediacy are increasingly prioritised characteristics of news production. As online news has developed, the idea of a single news item has been replaced by fast-changing content and new repertoires of constructing ‘Breaking News’. Whereas most research of online news has used synchronic rather than diachronic methods, this article introduces a new approach, which we choose to call Regular Interval Content Capture (RICC). The data produced by RICC enables dynamic online media texts to be studied as they are produced, edited, and changed using both quantitative and qualitative methods. In our study, the US ‘Crucial Tuesday’ primary elections serve as the empirical example. From a discourse analytical perspective, we analyse a total of 64 hours of online news flows collected from the US and International editions of CNN.com. The RICC approach allows us to find major representational differences between the two editions. Three different modes of writing, characterising different stages of CNN’s reporting, were identified.
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Stored only when both papers are in the corpus — 26 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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