Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Robust bounds for classification via selective sampling
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Tweets and Votes: A Study of the 2011 Singapore General Election
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
Novel Fisher discriminant classifiers
Pattern Recognition
Predicting the 2011 dutch senate election results with Twitter
Proceedings of the Workshop on Semantic Analysis in Social Media
Los Twindignados: The Rise of the Indignados Movement on Twitter
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
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Twitter is one of the most popular micro-blogging services in the world, often studied in the context of political opinion mining for its peculiar nature of online public discussion platform. In our work we analyse the phenomenon of political disaffection defined as the "lack of confidence in the political process, politicians, and democratic institutions, but with no questioning of the political regime". Disaffection for organised political parties and institutions has been object of studies and media attention in several Western countries. Especially the Italian case has shown a wide diffusion of this attitude. For this reason, we collect a massive database of Italian Twitter data (about 35 millions of tweets from April 2012 to October 2012) and we exploit scalable state-of-the-art machine learning techniques to generate time-series concerning the political disaffection discourse. In order to validate the quality of the time-series generated, we compare them with indicators of political disaffection from public opinion surveys. We find political disaffection on Twitter to be highly correlated with the indicators of political disaffection in the public opinion surveys. Moreover, we show the peaks in the time-series are often generated by external political events reported on the main newspapers.