2014/04/25 by Antònia Tugores, Tugores, Antònia, Pere Colet +1 · 4 citations
Computer Science · Engineering · Social Sciences · #Computer science #Data Management and Algorithms #Data science #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Programming Languages (cs.PL) #Programming language #Python (programming language) #Social and Information Networks (cs.SI) #Traffic Prediction and Management Techniques #World Wide Web #cs.PL #cs.SI
paper · pdf · doi:10.48550/arxiv.1404.6966
published in arXiv (Cornell University) (Cornell University) · Part of the Proceedings of the 6th European Conference on Python in Science (EuroSciPy 2013), Pierre de Buyl and Nelle Varoquaux editors, (2014)
arxiv created 2014/04/25 · openalex publication_date 2014/04/25 · arxiv updated 2014/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.