Exploring Social Media for Event Attendance

de Lira, V. M., Macdonald, C. , Ounis, I. , Perego, R., Renso, C. and Times, V. C. (2017) Exploring Social Media for Event Attendance. In: The 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Sydney, Australia, 31 Jul - 03 Aug 2017, pp. 447-450. ISBN 9781450349932 (doi: 10.1145/3110025.3110080)

Abstract

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Macdonald, Professor Craig and Ounis, Professor Iadh
• Authors: de Lira, V. M., Macdonald, C., Ounis, I., Perego, R., Renso, C., and Times, V. C.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450349932
• Copyright Holders: Copyright © 2017 Association for Computing Machinery
• First Published: First published in The 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): 447-450
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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