Predicting Public Unrest Using Social Networks, Based on Machine Learning in the Natural Language Processing

Today, the interest in predicting and detecting events using the data available on social networks has increased. Social networks can be called the sensors of society, because the users always express their positive and negative opinions about the events of the world around them, which results in an environment full of real-time reactions to real-world events. Social networks are one of the best tools for assessing the society and predicting upcoming events. Although the automatic detection and classification of events, especially social anomalies such as riots, is a trivial task, it is of great value to governments and security organizations that need to respond quickly and appropriately; because the costs and damages caused by these unrests can be reduced. For this challenge, we have developed an event predicting framework that can distinguish "disruptive events" that threaten social security and order from daily events. To do this, we have used natural language processing techniques to comprehend texts, remove the limitations of human language, and perform sentiment analysis and topic detection. We have classified the events using machine learning techniques such as the Naïve Bayes and Support Vector Machines. Finally, we have evaluated our framework in a large and real data set from Twitter to show the efficiency and effectiveness of our system in predicting future events. The results show that the proposed framework has the ability to detect tweets reflecting dissatisfaction with 79% accuracy. We have also managed to extract the useful information related to an event with 40% accuracy from these tweets in the form of 5 topics namely, the place, time, people, goals and event related factors.