  Graph structures are powerful tools for modeling the relationships between
textual elements. Graph-of-Words (GoW) has been adopted in many Natural
Language tasks to encode the association between terms. However, GoW provides
few document-level relationships in cases when the connections between
documents are also essential. For identifying sub-events on social media like
Twitter, features from both word- and document-level can be useful as they
supply different information of the event. We propose a hybrid Graph-of-Tweets
(GoT) model which combines the word- and document-level structures for modeling
Tweets. To compress large amount of raw data, we propose a graph merging method
which utilizes FastText word embeddings to reduce the GoW. Furthermore, we
present a novel method to construct GoT with the reduced GoW and a Mutual
Information (MI) measure. Finally, we identify maximal cliques to extract
popular sub-events. Our model showed promising results on condensing
lexical-level information and capturing keywords of sub-events.
