  Twitter serves as a data source for many Natural Language Processing (NLP)
tasks. It can be challenging to identify topics on Twitter due to continuous
updating data stream. In this paper, we present an unsupervised graph based
framework to identify the evolution of sub-topics within two weeks of
real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL)
with a node removal method to identify optimal graph clusters from temporal
Graph-of-Words (GoW). Subsequently, we model the clustering transitions between
the temporal graphs to identify the topic evolution. Finally, the transition
flows generated from both computational approach and human annotations are
compared to ensure the validity of our framework.
