Social Networks are becoming very popular sources of all kind of data. They allow a wide range of users to interact, socialize and express spontaneous opinions. The overwhelming amount of exchanged data on businesses, companies and governments make it possible to perform predictions and discover trends in many domains. In this paper we propose a new prediction model for the stock market movement problem based on collective classification. The model is using a number of public mood states as inputs to predict Up and Down movement of stock market. The proposed approach to build such a model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. A particular implementation of our approach is based on Ant Colony Optimization algorithm and customized for individual Bayesian classifiers. Our approach is validated with data collected from social media on the stock of a prestigious company. Promising results of our approach are compared with four alternative prediction methods namely, bagging, Adaboost, best expert, and expert trained on all the available data.