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

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.

Computation and Language (cs.CL)

arXiv:2509.22030 [cs.CL]

(or arXiv:2509.22030v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2509.22030 Focus to learn more arXiv-issued DOI via DataCite

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