This course introduces students to the theories, concepts and measures of Social Network Analysis (SNA), that is aimed at characterizing the structure of large-scale Online Social Networks (OSNs). The course presents both classroom teaching to introduce theoretical concepts, and hands-on computer work to apply the theory on real large-scale datasets obtained from OSNs like Facebook and Twitter.
This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.
This module introduces the main methods of analysis and mining of opinions and personal evaluations for users based on Big Data generated on the web or other sources. Emphasis will be put on text mining method applied to text originated on social media. Lessons will be supported by case studies developed in the SoBigData.eu lab.
The purpose of the course is to introduce the main analysis techniques for spatio-temporal data, with a particular focus on human mobility (including vehicles), aimed to better understand the overall mobility of a territory. The presentation will be supported by several case studies developed with the SoBigData.eu laboratory.
This module presents how to analyse traces that users leave from querying Web search engines (query log). It presents the main applications of Web mining including: i) how to profile the interests/activities of users, ii) how to use information from query logs for forecasting social indicators and optimizing Web search engines. Teaching activities will be supported by several case studies developed in the SoBigData.eu laboratory.
Stefano received his Bachelor’s and Master’s degrees in Computer Engineering from the University of Pisa respectively in 2007 and 2013. He also received a post-graduate Master’s degree in Big Data Analytics & Social Mining from the University of Pisa in 2016. Currently, he is a PhD Student at the Information Engineering School of the University of Pisa. He is also a Research Fellow at the Institute of Informatics and Telematics (IIT) of the National Research Council (CNR) in Pisa, Italy.
Roberto Trasarti was born in 1979 in Italy. He graduated in Computer Science in 2006, at the University of Pisa. He discussed his thesis on ConQueSt: a Constraint-based Query System aimed at supporting frequent patterns discovery. He started the Ph.D. in Computer Science at the School for Graduate Studies "Galileo Galilei", (University of Pisa). In June 2010 he received his Ph.D. presenting the thesis entitled "Mastering the Spatio-Temporal Knowledge Discovery Process".
Davide Gazzè is a Software Architect at Integris S.r.l. He is working on the developing of a Big Data platform for text analysis. On 2015, he took a Ph.D. at Leonardo da Vinci school with a thesis in “Social Media Monitoring and Analysis: Multi-domain Perspectives”. From 2010 to 2015, he worked at the Institute of Informatics and Telematics at CNR of Pisa, where he was involved on crawling and analysis of Social Media data in fields like reputation analysis, OSING and touristic flow analysis.
I graduated from Scuola Internazionale Superiore di Studi Avanzati di Trieste with a master's in Science Communication. Currently I am collaborating with various organizations in the field of science communication. I specifically write about science and technology subjects on many national newspapers such as La Repubblica and L'Espresso.
Valerio Arnaboldi holds a PhD in Computer Engineeging from the University of Pisa and he works as a research assistant in the Ubiquitous Internet group, at the institute for informatics and telematics (IIT) of the National Research Council of Italy (CNR). He works in the area of the analysis and characterisation of social structures and information dissemination in Online Social Networks, and on context- and social-aware middleware solutions for Mobile Social Networking applications.
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