User activity profiling with multi-layer analysis

Abstract

In this paper, we are interested in discovering semantically meaningful communities from a single user's perspective. We define a multi-layer analysis problem to derive a user's activity profile. Such an activity profile would include what activity areas a user is involved with, how important each activity is to the user, and who else is involved with the user on each activity as well as each participant's participation level. We believe a semantically meaningful community (corresponding to an activity area) must also consider the topics of the social messages rather than only the social links. While it is possible to use a hybrid approach based on traditional topic modeling, in this paper we propose a unified user modeling approach based on direct clustering over the social messages taking into considerations of both social connections and topics of social messages. Our clustering algorithm can be performed in a unified way in a unsupervised fashion as well as semi-supervised fashion when the user wants to give our algorithm some seeding inputs on his viewpoints. Moreover, when the new data comes, our algorithm can perform incremental updates on the new data without re-clustering the old data. Our experiments on social media datasets available from both within an enterprise and public social network demonstrate the effectiveness of our approach.