On-line social networks have grown quickly over the last few years and nowadays many people use them frequently. Furthermore the emergence of smartphones allows to access these networks any time from any physical location. Among the social networks, Twitter offers a particularly large set of data publicly available. Here we discuss the procedure to mine this data and store it in distributed databases using Python scripts. We also illustrate how geolocated tweets can be used to study the mobility of people in urban areas.
@article{arxiv.1404.6966,
title = {Mining online social networks with Python to study urban mobility},
author = {Antònia Tugores and Pere Colet},
journal= {arXiv preprint arXiv:1404.6966},
year = {2014}
}
Comments
Part of the Proceedings of the 6th European Conference on Python in Science (EuroSciPy 2013), Pierre de Buyl and Nelle Varoquaux editors, (2014)