Publications

Twitter is a massive platform for open communication between diverse groups of people. While traditional media segregates the world's population on lines of language, age, physical location, social status, and many other characteristics, Twitter cuts through these divides. The result is an extremely diverse social network. In this work, we combine features of this network structure with content analytics on the tweets in order to create a content + context network, capturing the relations not only between people, but also between people and content and between content and content. This rich structure allows deep analysis into many aspects of communication over Twitter. We focus on predicting user classifications by using relational probability trees with features from content + context networks. Experiments demonstrate that these features are salient and complementary for user classification.