NEClass: a lightweight LLM pipeline for measuring Foreign news coverage

Understanding cross-national news coverage is central to international communication research. However, quantifying how countries appear in the news remains methodologically challenging: simple keyword counts lack context, while manual coding does not scale. This paper introduces NEClass, a reproducible, open-source pipeline that combines standard Named Entity Recognition (NER) with a fine-tuned Large Language Model (LLM) to classify entities’ country affiliations. Unlike static dictionary approaches, NEClass leverages the latent world knowledge of modern LLMs and local context to resolve ambiguities dynamically. I demonstrate the pipeline’s validity regarding country attribution (weighted F1 of 0.95), show that it performs more reliably than established baseline approaches (including keyword matching and zero-shot LLM prompting), and illustrate its utility through a case study of the Eurozone crisis coverage in German and Italian newspapers. Ultimately, NEClass provides researchers with a robust and accessible toolkit to operationalize international attention patterns beyond the limitations of purely keyword-based metrics.