Name
Abstract | ||
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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.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3110025.3110080 | ASONAM '17: Advances in Social Networks Analysis and Mining 2017
Sydney
Australia
July, 2017 |
Keywords | Field | DocType |
event attendance,social media fora,nongeotagged posts,machine-learned classifiers,temporal content,social content,Creamfields events,attendance classifier,textual content,multimedia content,VFestival event | Social psychology,Computer science,Artificial intelligence,Classifier (linguistics),Attendance,Feature vector,Social media,Information retrieval,Microblogging,Exploit,Temporal periods,Word2vec,Machine learning | Conference |
ISSN | ISBN | Citations |
2473-9928 | 978-1-4503-4993-2 | 2 |
PageRank | References | Authors |
0.42 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinicius Monteiro de Lira | 1 | 4 | 2.57 |
| Craig Macdonald | 2 | 2588 | 178.50 |
| Iadh Ounis | 3 | 3438 | 234.59 |
| Raffaele Perego | 4 | 1471 | 108.91 |
| Chiara Renso | 5 | 925 | 76.04 |
| Valéria Cesário Times | 6 | 182 | 27.52 |