  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.
