A correlation-based approach for event detection in Instagram

Abstract

Online social networks like Instagram has more than 600 million users and creates over 300 million new posts every day. All those data can be used to detect real world events. Many works have been proposed in the literature to detect such events using different techniques, but this task is still ha rd. It involves many challenges including the processing of large volumes of data, the lack of a ground truth and the need for an adaptive approach. In this sense, our work attempts to tackle these problems with a semi-supervised learning approach to overcome those challenges using times series from Instagram posts. Experimental studies demonstrate that similar time series can be used to generalize the knowledge and predict the occurrence of an event. Also, we demonstrate that Support Vector Regression is a good alternative to Gaussian Process Regression as the first provides good results using much less computing resources than the second. Moreover, we made our labeled dataset public, hoping it can be useful to other researchers as well.