Prediction of Twitter Traffic Based on Machine Learning and Data Analytics

With the rapidly increasing number of online social network (OSN) users, the study of OSN application-specific mobile network traffic has attracted many research efforts in recent years. In this work, we study the temporal characteristics of Twitter traffic and propose a Twitter traffic prediction framework which combines statistical analytics and machine learning techniques. Experimental results based on real-world Twitter traffic dataset collected in central London have shown that the proposed framework has a high prediction accuracy with low computation complexity and low demand for the size of the dataset.