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arXiv · 2509.22030

From Outliers to Topics in Language Models: Anticipating Trends in News Corpora

Evangelia Zve·Benjamin Icard·Alice Breton·Lila Sainero·Gauvain Bourgne·Jean-Gabriel Ganascia
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Abstract

This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. Using vector embeddings from state-of-the-art language models and a cumulative clustering approach, we track their evolution over time in French and English news datasets focused on corporate social responsibility and climate change. The results reveal a consistent pattern: outliers tend to evolve into coherent topics over time across both models and languages.

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Evangelia Zve, Benjamin Icard, Alice Breton, Lila Sainero, Gauvain Bourgne, Jean-Gabriel Ganascia. 2025-09-26. From Outliers to Topics in Language Models: Anticipating Trends in News Corpora. https://arxiv.org/abs/2509.22030

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