The world-wide size of social networks, such as Facebook and Twitter, is making possible to analyse the realtime behaviour of large groups of people, such those attending popular events. This paper presents work and results on the analysis of geotagged tweets carried out to understand the behaviour of people attending the 2014 FIFA World Cup. We monitored the Twitter users attending the World Cup matches to discover the most frequent movements of fans during the competition. The data source is represented by all geotagged tweets collected during the 64 matches of the World Cup from June 12 to July 13, 2014. For each match we considered only the geotagged tweets whose coordinates fallen within the area of stadiums, during the matches. Then, we carried out a trajectory pattern mining analysis on the set of the tweets considered. Original results were obtained in terms of number of matches attended by groups of fans, clusters of most attended matches, and most frequented stadiums.

Following soccer fans from geotagged tweets at FIFA World Cup 2014

Eugenio Cesario;
2015

Abstract

The world-wide size of social networks, such as Facebook and Twitter, is making possible to analyse the realtime behaviour of large groups of people, such those attending popular events. This paper presents work and results on the analysis of geotagged tweets carried out to understand the behaviour of people attending the 2014 FIFA World Cup. We monitored the Twitter users attending the World Cup matches to discover the most frequent movements of fans during the competition. The data source is represented by all geotagged tweets collected during the 64 matches of the World Cup from June 12 to July 13, 2014. For each match we considered only the geotagged tweets whose coordinates fallen within the area of stadiums, during the matches. Then, we carried out a trajectory pattern mining analysis on the set of the tweets considered. Original results were obtained in terms of number of matches attended by groups of fans, clusters of most attended matches, and most frequented stadiums.
2015
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Social network analysis
Twitter
Geographical data mining
Trajectory pattern mining
FIFA World Cup
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/303097
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