Machine Learning Techniques to Predict Pandemic from Social Media

Abstract

In recent years, there has been a particular focus on improving public health through the means of prediction and preparedness of pandemic diseases. Early detection, prediction, and analysis of disease outbreaks allow the authority agencies to mitigate the side effects of Pandemic and immune the people. Nowadays, social media such as Twitter or Facebook play a vital role in the crisis situation. By means of social media, people from all over the world can be aware of the recent pandemic outbreaks. In fact, the mainstream adoption of social media in people daily life has caused a paradigm shift in how people communicate, create, cooperate, and use information during a crisis. Moreover, by analyzing data, which broad-casted during a crisis, the relevant health organizations or agents can discover much useful information. On the other side, the volume and velocity of messages or tweets during crises today tend to be extremely high and make it hard for discerning and taking an actions. Therefore, machine-learning techniques can be used to help analyzing this big flow of messages. They are the useful way to discovering the knowledge from big data. Various methods and machine learning algorithms have been proposed and applied in various cases. In this work, we adopted data analysis and predictive techniques. Several features from tweets have been extracted and data is modeled by binary classification for pandemic prediction. Three different predictive models (Support Vector Machine, Decision Tree, and Naive Bayes) have been conducted in order to pandemic prediction. Our experimental results illustrate that SVM technique outperforms other techniques. However, there is no global best predictive model and it depends on various parameters such as dataset, configuration, etc.