Event Detection in Social Media Through Phase Transition of Bigrams Entropy

Abstract

A social network is a valuable source of data that is useful to understand a wide range of events happening around the world, with each user being a potential contributor to accomplish this task. This work proposes a novel method to detect events in Twitter based on the calculation of entropy of the content of tweets in order to classify the most shared topic as an event or not. We observed that the entropy of the bigrams extracted from tweets are subject to a continuous phase transition when social media users start to react and interact with an event that is taking place. Hence, we propose a method to detect this phase transition, and consequently detect an event, and extract the keywords related to the corresponding event. We compared the performance of our method to other approaches of the literature and we observed that our method is the more regular among three metrics and reached the best overall performance. Furthermore, we present evidence that our method is very sensitive to correctly detect events that occur almost at same time.