# BERTrend: Neural Topic Modeling for Emerging Trends Detection

arXiv: https://arxiv.org/abs/2411.05930 · HTML: https://mlsanity.com/paper/2411.05930/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 8 Nov 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [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)
- [Dynamic Word Embeddings for Evolving Semantic Discovery](https://mlsanity.com/paper/1703.00607/) (2017)
- [Discriminative Topic Mining via Category-Name Guided Text Embedding](https://mlsanity.com/paper/1908.07162/) (2019)
- [DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM](https://mlsanity.com/paper/2310.15296/) (2023)

## Built on by (most cited first, 13 of 13)

- [From Noise to Signal: When Outliers Seed New Topics](https://mlsanity.com/paper/2603.18358/) (2026)
- [Agentic Retrieval of Topics and Insights from Earnings Calls](https://mlsanity.com/paper/2507.07906/) (2025)
- [Continual Neural Topic Model](https://mlsanity.com/paper/2508.15612/) (2025)
- [Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces](https://mlsanity.com/paper/2509.01051/) (2025)
- [From Outliers to Topics in Language Models: Anticipating Trends in News Corpora](https://mlsanity.com/paper/2509.22030/) (2025)
- [Paper Espresso: From Paper Overload to Research Insight](https://mlsanity.com/paper/2604.04562/) (2026)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Noise is Signal: Density-Based Outliers as Leading Indicators of Occupational Emergence in Labor Market Text](https://mlsanity.com/paper/2606.22769/) (2026)
- [Forecasting Technological Directions in Wireless Networks and Mobile Computing via AutoML Framework](https://mlsanity.com/paper/2606.27394/) (2026)
- [AutoCluster, AutoTopicModeling, AutoTrendAnalysis: A Complete AutoML Pipeline for Predicting Emerging Trends](https://mlsanity.com/paper/2607.22641/) (2026)
- [C-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning](https://mlsanity.com/paper/2608.26870/) (2026)
- [BackTrend: Evaluating Scientific Weak-Signal Prediction via Backward Reconstruction](https://mlsanity.com/paper/2609.24921/) (2026)
- [Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space](https://mlsanity.com/paper/2609.29183/) (2026)

## Abstract (arXiv)

Detecting and tracking emerging trends and weak signals in large, evolving text corpora is vital for applications such as monitoring scientific literature, managing brand reputation, surveilling critical infrastructure and more generally to any kind of text-based event detection. Existing solutions often fail to capture the nuanced context or dynamically track evolving patterns over time. BERTrend, a novel method, addresses these limitations using neural topic modeling in an online setting. It introduces a new metric to quantify topic popularity over time by considering both the number of documents and update frequency. This metric classifies topics as noise, weak, or strong signals, flagging emerging, rapidly growing topics for further investigation. Experimentation on two large real-world datasets demonstrates BERTrend's ability to accurately detect and track meaningful weak signals while filtering out noise, offering a comprehensive solution for monitoring emerging trends in large-scale, evolving text corpora. The method can also be used for retrospective analysis of past events. In addition, the use of Large Language Models together with BERTrend offers efficient means for the interpretability of trends of events.