Nowadays, the analysis of data from Online Social Networks (OSNs) is one of the main areas of interest for companies involved in data analysis. A particular type of OSNs are \ac{LBSN}s, which in addition to providing the normal functions of social networks add location-based services. This research aims to analyze the data of one of the most widely used LSBNs at the moment, namely Twitter, and calculate the entropy variation of the latter to identify any anomalies in different geographical areas. A careful analysis has been carried out on what is currently the panorama of data analysis made on the most used social networks at the moment.