Document - Exploring social media for event attendance

2017

Conference article Open Access

Exploring social media for event attendance

Monteiro De Lira V., Macdonald C., Ounis I., Perego R., Renso C., Cesario Times V.

Event attendance Prediction Social media analysis

Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ~87% accuracy to classify users that will participate in the event.

Source: ASONAM '17 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 447–450, Sydney, Australia, July 31 - August 03, 2017

Publisher: ACM - Association for Computing Machinery, New York, USA

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:381018,
	title = {Exploring social media for event attendance},
	author = {Monteiro De Lira V. and Macdonald C. and Ounis I. and Perego R. and Renso C. and Cesario Times V.},
	publisher = {ACM - Association for Computing Machinery, New York, USA},
	doi = {10.1145/3110025.3110080},
	booktitle = {ASONAM '17 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 447–450, Sydney, Australia, July 31 - August 03, 2017},
	year = {2017}
}