New framework enables cross-source topic comparison using shared taxonomy

Researchers have developed a novel framework to address the challenge of comparing topic attention across different media sources. This framework creates a single, shared topic space by aligning corpus-specific topic models using the IPTC Media Topics taxonomy. The method, tested on a New York Times corpus, demonstrated superior mapped coverage compared to zero-shot benchmarks and showed gradual coverage decline as assignment thresholds were tightened. AI

IMPACT Provides a standardized method for comparing topic trends across diverse datasets, potentially improving media analysis and information retrieval.

RANK_REASON Academic paper detailing a new methodology for topic modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

• alphaXiv
• arXiv
• BERTopic
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• IPTC Media Topics
• New York Times 2011
• ScienceCast

• paper
• other

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