  This paper introduces a temporal framework for detecting and clustering
emergent and viral topics on social networks. Endogenous and exogenous
influence on developing viral content is explored using a clustering method
based on the a user's behavior on social network and a dataset from Twitter
API. Results are discussed by introducing metrics such as popularity,
burstiness, and relevance score. The results show clear distinction in
characteristics of developed content by the two classes of users.
