The increasing use of social networks generates enormous amounts of data that can be used for various types of analysis. Some of these data have temporal and geographical information, which can be used for comprehensive examination. In this paper, the authors propose a new method to analyze the massive volume of messages available in Twitter to identify places in the world where events such as TV shows, climate change, disasters, and sports are emerging. The proposed approach is based on a neural network used to detect outliers from a time series, which is built upon statistical data from tweets located in different political divisions (i.e., countries, cities). These outliers are used to identify localized events within an abnormal behavior in Twitter.