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Classifying Short Messages on Social Networks using Vector Space Models

In this paper we propose a method to classify irrelevant messages and filter them out before they are published on a social network. Previous works tended to focus on the consumer of information, whereas the publisher of a message has the challenge of addressing all of his or her followers or subscribers at once. In our method, a supervised learning task, we propose vector space models to train a classifier with labeled messages from a user account. We test the precision and accuracy of the classifier on over 13,000 Twitter accounts. Results show the feasibility of our approach on most types of active accounts on this social network.