arXiv Computation and Language September 25, 2026 By Evangelia Zve, Gauvain Bourgne, Jean-Gabriel Ganascia

Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space

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The study investigates whether documents initially classified as noise in embedding-based topic models can be identified as precursors to emerging topics. By labeling documents based on their future trajectories and measuring confidence across multiple embedding models, the authors find that anticipatory outliers are predictable at publication time, achieving an F1 score above 0.90 on high-consensus subsets and 0.76–0.80 in chronological evaluation. The predictive power largely stems from geometric features that capture each outlier’s position in embedding space.

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