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 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).

[1]  Yamir Moreno,et al.  Structural and Dynamical Patterns on Online Social Networks: The Spanish May 15th Movement as a Case Study , 2011, PloS one.

[2]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[3]  Marco Gonzalez,et al.  Author's Personal Copy Social Networks Tastes, Ties, and Time: a New Social Network Dataset Using Facebook.com , 2022 .

[4]  Lada A. Adamic,et al.  The Party Is Over Here: Structure and Content in the 2010 Election , 2011, ICWSM.

[5]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[6]  G. Caldarelli,et al.  Preferential attachment in the growth of social networks, the Internet encyclopedia wikipedia , 2007 .

[7]  Daniel Gayo-Avello,et al.  "I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" - A Balanced Survey on Election Prediction using Twitter Data , 2012, ArXiv.

[8]  Guido Caldarelli,et al.  Web Search Queries Can Predict Stock Market Volumes , 2011, PloS one.

[9]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[10]  Blesson Varghese,et al.  The royal birth of 2013: Analysing and visualising public sentiment in the UK using Twitter , 2013, 2013 IEEE International Conference on Big Data.

[11]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[12]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[13]  Maria Pinto,et al.  What can Social Media teach us about protests? Analyzing the Chilean 2011-12 Student Movement's Network evolution through Twitter data , 2013, ArXiv.

[14]  David M. Pennock,et al.  Using internet searches for influenza surveillance. , 2008, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[15]  Jacob Ratkiewicz,et al.  Detecting and Tracking Political Abuse in Social Media , 2011, ICWSM.

[16]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[17]  A. Pentland,et al.  Computational Social Science , 2009, Science.

[18]  V. Zlatic,et al.  Wikipedias: collaborative web-based encyclopedias as complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  D. Butler Web data predict flu , 2008, Nature.

[20]  Sune Lehmann,et al.  Understanding the Demographics of Twitter Users , 2011, ICWSM.

[21]  Zizi Papacharissi,et al.  The virtual geographies of social networks: a comparative analysis of Facebook, LinkedIn and ASmallWorld , 2009, New Media Soc..

[22]  Ee-Peng Lim,et al.  Tweets and Votes: A Study of the 2011 Singapore General Election , 2012, 2012 45th Hawaii International Conference on System Sciences.

[23]  M. Waldrop,et al.  Community cleverness required , 2008, Nature.

[24]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[25]  Howard Rosenbaum,et al.  Effects of reading proficiency on embedded stem priming in primary school children , 2021 .

[26]  A flood of hard data , 2008, Nature.

[27]  Yamir Moreno,et al.  The Dynamics of Protest Recruitment through an Online Network , 2011, Scientific reports.

[28]  Alessandro Vespignani,et al.  Beating the news using social media: the case study of American Idol , 2012, EPJ Data Science.

[29]  Johan Bos,et al.  Predicting the 2011 Dutch Senate Election Results with Twitter , 2012 .

[30]  J. Bollen,et al.  More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior , 2013, PloS one.

[31]  Panagiotis Takis Metaxas,et al.  How (Not) to Predict Elections , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[32]  A. J. Morales,et al.  Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential Election as a case study , 2012, Chaos.