relation: http://eprints.imtlucca.it/3489/
title: Electoral predictions with Twitter: a machine-learning approach
creator: Coletto, Mauro
creator: Lucchese, Claudio
creator: Orlando, Salvatore
creator: Perego, Raffaele
subject: QA75 Electronic computers. Computer science
description: 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.
date: 2015
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nd
identifier: http://eprints.imtlucca.it/3489/1/paper_19.pdf
identifier:   Coletto, Mauro and Lucchese, Claudio and Orlando, Salvatore and Perego, Raffaele  Electoral predictions with Twitter: a machine-learning approach.  In: 6th Italian Information Retrieval Workshop, 25-26 May 2015, Cagliari, Italy         (2015)  
relation: http://ceur-ws.org/Vol-1404/paper_19.pdf