# Measuring LDA Topic Stability from Clusters of Replicated Runs

arXiv: https://arxiv.org/abs/1808.08098 · HTML: https://mlsanity.com/paper/1808.08098/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [Are Neural Topic Models Broken?](https://mlsanity.com/paper/2210.16162/) (2022)
- [No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning](https://mlsanity.com/paper/2303.14443/) (2023)
- [Determination of the Number of Topics Intrinsically: Is It Possible?](https://mlsanity.com/paper/2406.10402/) (2024)
- [Multi-Label Topic Model for Financial Textual Data](https://mlsanity.com/paper/2311.07598/) (2023)
- [Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms](https://mlsanity.com/paper/2312.01687/) (2023)
- [Repeatability is not recovery: Quantifying algorithmic stability and topic recovery in Latent Dirichlet Allocation](https://mlsanity.com/paper/2511.12850/) (2025)
- [The Many Morals of AI: The cautionary bias of North American news narratives in global perspective](https://mlsanity.com/paper/2610.18573/) (2026)

## Abstract (arXiv)

Background: Unstructured and textual data is increasing rapidly and Latent Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods for it. Past work suggests that instability of LDA topics may lead to systematic errors. Aim: We propose a method that relies on replicated LDA runs, clustering, and providing a stability metric for the topics. Method: We generate k LDA topics and replicate this process n times resulting in n*k topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k clusters now represent the original LDA topics and we present them like normal LDA topics showing the ten most probable words. For the clusters, we try multiple stability metrics, out of which we recommend Rank-Biased Overlap, showing the stability of the topics inside the clusters. Results: We provide an initial validation where our method is used for 270,000 Mozilla Firefox commit messages with k=20 and n=20. We show how our topic stability metrics are related to the contents of the topics. Conclusions: Advances in text mining enable us to analyze large masses of text in software engineering but non-deterministic algorithms, such as LDA, may lead to unreplicable conclusions. Our approach makes LDA stability transparent and is also complementary rather than alternative to many prior works that focus on LDA parameter tuning.