Abstract

The development of social networks such as Twitter, Facebook and Google+ allow users to share their beliefs, feelings, or observations with their circles of friends. Based on these data, a range of applications and techniques has been developed, targeting to provide a better quality of life to the users. Nevertheless, the quality of results of the geolocationaware applications is signicantly restricted due to the tiny percentage of the social media data that is geotagged ( 2% for Twitter). Hence, increasing this percentage is an important and challenging problem. Moreover, information extracted from social media data can be complemented by the analysis of mobile phone usage data, in order to provide further insights on human activity patterns. In this thesis, we present a novel method for analyzing and geolocalizing non-geotagged Twitter posts. The proposed method is the rst to do so at the ne-grain of city neighborhoods, while being both eective and time ecient. Our method is based on the extraction of representative keywords for each candidate location,as well as the analysis of the tweet volume time series. We also describe a system built on top of our method, which geolocalizes tweets and allows users to visually examine the results and their evolution over time. Our system allows the user to get a better idea of how the activity of a particular location changes, which the most important keywords are, as well as to geolocalize individual tweets of interest. Moreover, we study the activity and mobility characteristics of the users that post geotagged tweets and compared the mobility of users who attended the event with a random set of users. Interestingly, the results of this analysis indicate that a very small number of users (i.e., less than 35 users in this study) is able to represent the mobility patterns present in the entire dataset. Finally, we study the call activity and mobility patterns, clustering the observed behaviors that exhibited similar characteristics, and characterizing the anomalous behaviors. We analyzed a Call Detail Record (CDR) dataset, containing (aggregated) information on the calls among mobile phones. Employing density-based algorithms and statistical analysis, we developed a framework that identies abnormal locations, as well abnormal time intervals. The results of this work can be used for early identication of exceptional situations, monitoring the eects of important events in urban and transportation planning, and others.