Informative vs. Non-informative Short Message Detection in Social Networks

We propose a method for classifying tweet messages into two classes: informative and non-informative. We consider informative messages those that contain information that interest the public, trends, events and news. Non-informative tweets are personal messages that do not interest the public, like conversations between friends, feelings and description of mood. The motivation of our work is keeping informative tweets that contain essential information, and filtering out useless tweets. Real applications that can benefit from our work are trend/topic detection applications, recommendation systems and applications that make predictions based on user messages on social networks.Challenges of processing tweet messages is that they are short messages, unstructured with unclear topic. We propose a weighted variation of the binary multinomial naive Bayes' model to identify informative messages. We train our classifier and we evaluate results using 5-fold and 10-fold cross validation. We compare the results with the original binary multinomial naive Bayes model. We use two independent datasets of tweet messages crawled from the web. We evaluate and present our results using the following metrics: accuracy, recall, specificity, F-measure with its variations (F2 score and F0.5 score).