  In this paper, we present computational models to predict Twitter users'
attitude towards a specific brand through their personal and social
characteristics. We also predict their likelihood to take different actions
based on their attitudes. In order to operationalize our research on users'
attitude and actions, we collected ground-truth data through surveys of Twitter
users. We have conducted experiments using two real world datasets to validate
the effectiveness of our attitude and action prediction framework. Finally, we
show how our models can be integrated with a visual analytics system for
customer intervention.
