%O Published in "Proceedings of the 6th Italian Information Retrieval Workshop"
%B Proceedings of the 6th Italian Information Retrieval Workshop
%D 2015
%A Mauro Coletto
%A Claudio Lucchese
%A Salvatore Orlando
%A Raffaele Perego
%L eprints3489
%X Several studies have shown how to approximately predict public opinion,
such as in political elections, by analyzing user activities in blogging platforms
and on-line social networks. The task is challenging for several reasons.
Sample bias and automatic understanding of textual content are two of several
non trivial issues.
In this work we study how Twitter can provide some interesting insights concerning
the primary elections of an Italian political party. State-of-the-art approaches
rely on indicators based on tweet and user volumes, often including sentiment
analysis. We investigate how to exploit and improve those indicators in order to
reduce the bias of the Twitter users sample. We propose novel indicators and a
novel content-based method. Furthermore, we study how a machine learning approach
can learn correction factors for those indicators. Experimental results on
Twitter data support the validity of the proposed methods and their improvement
over the state of the art.
%C Cagliari, Italy
%T Electoral predictions with Twitter: a machine-learning approach