Country: Italy

Affiliation: Associate Professor of Computer Engineering at University of Calabria, Italy

Paolo Trunfio is an associate professor of computer engineering at DIMES Department, University of Calabria, Italy. He is also co-founder and managing director of DtoK Lab S.r.l., an Italian company that provides cloud solutions for Big Data analysis. He was visiting researcher at the Swedish Institute of Computer Science in Stockholm (2007) and a research collaborator at the Italian National Research Council (2001-2002). He has contributed to the organization of several scientific events, including: scientific coordinator of the Second NESUS Winter School & PhD Symposium 2017; local organizer of the 3rd COST 804 Training School on Energy Efficiency in Large Scale Distributed Systems 2013; PC vice chair of AINA 2013, “Distributed Database and Data Mining” track; PC local chair of EuroPar 2010, “Peer to Peer Computing” track. He has served in the program committee of more than 100 international conferences and workshops, including (for one or more editions): CCGrid, SMC, EuroPar, AINA, CBMS, CloudCom, HumanCom, ScalCom, GreenCom, TrustCom, CIT, HPCC, ISPA, ICA3PP, CLOSER, CSA, PDCS, CIIA, CODS, IDCS, ICSDM, CGC, IUCC. He is currently serving as associate editor of the IEEE Transactions on Cloud Computing and is a member of the editorial board of 8 scientific journals: Future Generation Computer Systems, Peer-to-Peer Networking and Applications, Journal of Big Data, International Journal of Web Science, International Journal of Cloud Computing, International Scholarly Research Notices, International Journal of Web Information Systems, and International Journal of Grid and Utility Computing. Dr. Trunfio is a member of the ACM since 2008.

Social Media Analysis for Trajectory Discovery in Large-Scale Events

Date: 28th September
Time: 9.30 AM
Abstract:

The widespread use of social media platforms such as Twitter and Instagram allows scientists to collect huge amount of data posted by people interested in a given topic or attending a popular event. This data can be analyzed to infer patterns and trends about people behaviors related to a topic or an event on a very large scale. Social media posts are often tagged with geographical coordinates or other information that allows identifying user positions, this way enabling the discovery of mobility patterns analysis using trajectory mining techniques. This lecture describes a methodology for discovering behavior and mobility patterns of users attending large-scale public events, by collecting and analyzing social media posts. The methodology is demonstrated through two case studies. The first one is an analysis of geotagged tweets for learning the behavior of people attending the 2014 FIFA World Cup. The second one is a mobility pattern analysis on the Instagram users who visited EXPO 2015. In both cases, a very high correlation was measured between official attendee numbers and those produced by our analysis, which shows the effectiveness of the proposed methodology and the accuracy of the results.