Scalable Twitter User Clustering Approach boosted by Personalized PageRank

Anup Naik, Vibhor Kanojia, Hideyuki Maeda, Sumio Fujita

PAKDD 2017, 2017/5


Natural Language Processing Information Retrieval Machine Learning Data Science Misc.

Twitter has been the focus of analysis in recent years due to various interesting and challenging problems, one of them being Clustering of its Users based on their interests. For graphs, there are many clustering approaches which look at either the structure or at its contents. However, when we consider real world data such as Twitter Data, structural approaches may produce many different user clusters with similar interests. Similarly, content-based clustering approaches on Twitter Data produce inferior results due limited length of Tweet and due to lots of garbled data. Hence, these approaches cannot be directly used for practical applications. In this paper, we have made an effort to cluster Twitter Users based on their interest, looking at both the structure of the graph generated using Twitter Data, as well as its contents. By combining these approaches, we improve our results compared to the existing techniques, thereby generating results befitting the practical applications.

Scalable Twitter User Clustering Approach boosted by Personalized PageRank(External Site Link)