arXiv:2509.22030v2 Announce Type: replace 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. U...
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.
The paper introduces a new method that uses embedding-based dynamic topic modeling to detect and measure topic drift at the comment level in a large dataset. By applying pretrained language models to generate contextualized embeddings for 12.7 billion Reddit comments from 2006 to 2022, the authors identify evolving topic clusters over time using unsupervised techniques. Their approach includes scalable modifications to existing methods and a null model comparison test, revealing that politically and socially contentious topics show significant directional drift, while areas like music and sports remain relatively stable.
BERTilda is an explainable framework for tracking topic lifecycles in longitudinal text streams. It discovers topics independently in each time window using an embedding‑based topic model, then links topics across adjacent windows via a temporal graph that uses both semantic similarity and a bidirectional coverage signal derived from tweet‑to‑topic attribution. The graph‑based rules identify continuations, splits, merges, disappearances, and unclear transitions, and the method achieves up to 87% agreement with human annotators on a gold‑standard subset.
arXiv:2606. 27394v1 Announce Type: cross Abstract: The exponential increase in scientific publications has driven the emergence of new trends.
TopiCLEAR is a framework that clusters document or sentence embeddings using adaptive dimensionality reduction to uncover low‑dimensional geometric structures that correspond to human‑interpretable topics. The method is evaluated on four benchmark datasets, showing strong agreement with human annotations, especially for short and informal texts. A Twitter case study demonstrates that TopiCLEAR yields more interpretable topics than LDA, recovering both annotated topic structure and coherent sub‑topics.
The paper introduces SeLATM, a framework that improves topic modeling by generating topics at the segment level and refining them through agentic feedback loops. This approach addresses limitations of LLM-based topic assignment methods, such as the inability to produce topic distributions, overly broad or narrow topics, and high resource consumption. Experiments on multiple datasets show that SeLATM reduces LLM resource usage while maintaining superior performance.
C-Unseen is a self‑interpretable framework designed to detect weak signals in Dynamic Temporal Knowledge Graphs (DTKGs). It defines a weak signal as a rare, semantically coherent subgraph that spreads across consecutive snapshots. The system uses a Rare Subgraphs Extractor, where a large language model identifies subgraphs that contrast with the dominant narrative through chain‑of‑thought reasoning, and a Weak Signal Alerter that tracks the persistence of these subgraphs over time to isolate true weak signals, outperforming keyword, topic, and graph‑based baselines.
The paper introduces MARETopic, a training‑free framework that identifies topics by selecting rank‑based prototype documents from pretrained embeddings. By projecting embeddings onto a low‑dimensional manifold and building ranked neighborhood lists, a greedy algorithm picks exactly K exemplar texts whose neighborhoods cover the corpus. Two variants—MARETopic_Corr, which uses a query‑performance predictor and rank correlation, and MARETopic_Diff, which employs a rank‑based diffusion matrix—achieve higher purity and NMI on benchmark datasets and run significantly faster, while also improving topic coherence and vocabulary diversity through a novel Maximal Marginal Relevance step.
Dynamic Embedded Topic Models (D-ETM) are extended to enable stable cross‑corpus temporal analysis by first learning a shared dynamic topic space—called the shared backbone—over a merged multi‑corpus collection. Corpus‑specific residual adaptation is then applied around this frozen backbone, allowing each corpus to specialize lexically without creating separate latent topic spaces. Experiments on three corpora spanning 97 years show that this approach yields much stronger alignment of topic trajectories (97.5 ± 0.7 % Retrieval@1) compared to full fine‑tuning or independent training with post‑hoc matching.
arXiv:2609.08200v1 Announce Type: new Abstract: Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing...
MonoTM is an interpretable topic modeling framework that separates the estimation of document–topic mixtures from the generation of topic descriptors. It uses sparse autoencoders to extract dense, interpretable features for mixture estimation, then learns topic descriptors from a distinct set of corpus‑grounded semantic features. This approach preserves global topic structure while providing more meaningful, semantic‑unit descriptors than traditional top‑word lists.