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

Computational Models for Attitude and Actions Prediction