# Modeling Fuzzy Cluster Transitions for Topic Tracing

arXiv: https://arxiv.org/abs/2104.08258 · HTML: https://mlsanity.com/paper/2104.08258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach](https://mlsanity.com/paper/1505.05657/) (2015)
- [Clustering Stream Data by Exploring the Evolution of Density Mountain](https://mlsanity.com/paper/1710.00867/) (2017)

## Abstract (arXiv)

Twitter can be viewed as a data source for Natural Language Processing (NLP) tasks. The continuously updating data streams on Twitter make it challenging to trace real-time topic evolution. In this paper, we propose a framework for modeling fuzzy transitions of topic clusters. We extend our previous work on crisp cluster transitions by incorporating fuzzy logic in order to enrich the underlying structures identified by the framework. We apply the methodology to both computer generated clusters of nouns from tweets and human tweet annotations. The obtained fuzzy transitions are compared with the crisp transitions, on both computer generated clusters and human labeled topic sets.