Large Scale Human Mobility Data Analysis through Social Media
Online Social Networks (OSN) are part of every day life of millions of people around the world. Users can instantly share content online which may be accessed by a selected list of friends or be publicly available. Location information is part of these data being shared which opens up possibilities for research in human mobility. For several years, this type of research relied on data from sensors (with small number of users/devices) or telecom operators (very hard to be obtained, mainly due to legal issues). Several studies have been made exploring location information from OSN but a thorough understanding of the limits of this approach have not yet been fully studied.
The student would be responsible for collecting data from different OSN and validate various human mobility studies published so far. Expose strengths and limitations of each analysis and validate/reject existing models.
Good background in programming and machine learning. Any experience with data analysis, R/Python/Java and databases is a plus.
Leonardo Tonetto (tonetto (at) in.tum.de)