A multi-level geographical study of Italian political elections from Twitter data.

In this paper we present an analysis of the behavior of Italian Twitter users during national political elections. We monitor the volumes of the tweets related to the leaders of the various political parties and we compare them to the elections results. Furthermore, we study the topics that are asso...

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Main Authors: Guido Caldarelli, Alessandro Chessa, Fabio Pammolli, Gabriele Pompa, Michelangelo Puliga, Massimo Riccaboni, Gianni Riotta
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4011699?pdf=render
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spelling doaj-f46b76a45a9f4caf9587663ec8536aae2020-11-25T01:11:57ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0195e9580910.1371/journal.pone.0095809A multi-level geographical study of Italian political elections from Twitter data.Guido CaldarelliAlessandro ChessaFabio PammolliGabriele PompaMichelangelo PuligaMassimo RiccaboniGianni RiottaIn this paper we present an analysis of the behavior of Italian Twitter users during national political elections. We monitor the volumes of the tweets related to the leaders of the various political parties and we compare them to the elections results. Furthermore, we study the topics that are associated with the co-occurrence of two politicians in the same tweet. We cannot conclude, from a simple statistical analysis of tweet volume and their time evolution, that it is possible to precisely predict the election outcome (or at least not in our case of study that was characterized by a "too-close-to-call" scenario). On the other hand, we found that the volume of tweets and their change in time provide a very good proxy of the final results. We present this analysis both at a national level and at smaller levels, ranging from the regions composing the country to macro-areas (North, Center, South).http://europepmc.org/articles/PMC4011699?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Guido Caldarelli
Alessandro Chessa
Fabio Pammolli
Gabriele Pompa
Michelangelo Puliga
Massimo Riccaboni
Gianni Riotta
spellingShingle Guido Caldarelli
Alessandro Chessa
Fabio Pammolli
Gabriele Pompa
Michelangelo Puliga
Massimo Riccaboni
Gianni Riotta
A multi-level geographical study of Italian political elections from Twitter data.
PLoS ONE
author_facet Guido Caldarelli
Alessandro Chessa
Fabio Pammolli
Gabriele Pompa
Michelangelo Puliga
Massimo Riccaboni
Gianni Riotta
author_sort Guido Caldarelli
title A multi-level geographical study of Italian political elections from Twitter data.
title_short A multi-level geographical study of Italian political elections from Twitter data.
title_full A multi-level geographical study of Italian political elections from Twitter data.
title_fullStr A multi-level geographical study of Italian political elections from Twitter data.
title_full_unstemmed A multi-level geographical study of Italian political elections from Twitter data.
title_sort multi-level geographical study of italian political elections from twitter data.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
description In this paper we present an analysis of the behavior of Italian Twitter users during national political elections. We monitor the volumes of the tweets related to the leaders of the various political parties and we compare them to the elections results. Furthermore, we study the topics that are associated with the co-occurrence of two politicians in the same tweet. We cannot conclude, from a simple statistical analysis of tweet volume and their time evolution, that it is possible to precisely predict the election outcome (or at least not in our case of study that was characterized by a "too-close-to-call" scenario). On the other hand, we found that the volume of tweets and their change in time provide a very good proxy of the final results. We present this analysis both at a national level and at smaller levels, ranging from the regions composing the country to macro-areas (North, Center, South).
url http://europepmc.org/articles/PMC4011699?pdf=render
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