PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Coletto M., Lucchese C., Orlando S., Perego R., Chessa A., Puliga M. Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study. In: ICCSS 2015 - International Conference on Computational Social Science (Helsinki, Finland, 8-11 June 2015).
 
 
Abstract
(English)
Several studies have shown how to approximately predict real-world phenomena, such as political elections, by analyzing user activities in micro-blogging platforms. This approach has proven to be interesting but with some limitations, such as the representativeness of the sample of users, and the hardness of understanding polarity in short messages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function.
Subject Twitter
Political
Elections
Prediction
Machine learning
Network analysis
H.2.8 Database Applications
68T99


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