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

arXiv: https://arxiv.org/abs/2509.22030 · HTML: https://mlsanity.com/paper/2509.22030/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [BERTrend: Neural Topic Modeling for Emerging Trends Detection](https://mlsanity.com/paper/2411.05930/) (2024)
- [Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization](https://mlsanity.com/paper/2307.11770/) (2023)
- [A Multi-Label Dataset of French Fake News: Human and Machine Insights](https://mlsanity.com/paper/2403.16099/) (2024)
- [Monitoring geometrical properties of word embeddings for detecting the emergence of new topics](https://mlsanity.com/paper/2111.03496/) (2021)

## Abstract (arXiv)

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.