IfLikeswerevotes…an empiricalstudy on Italianadministrativeelections<br />Fabio [.] Giglietto [@uniurb.it]<br />Department...
Summary<br />Case presentation;<br />Research hypothesis;<br />Literature review;<br />Methodology;<br />Results;<br />Con...
Case presentation<br />Administrative election 15/16 May 2011<br />9 provinces;<br />1177 municipalities (23 provincial ca...
Research hypothesis<br />H1: Is the popularity on Facebook a good predictor of a candidate electoral performance?<br />H2:...
Literature review (1/2)<br />A correlation between Facebook Likes and votes share was found in 2006 midterm elections and ...
Literature review (2/2)<br />After 2010 midterm election Facebook claimed that in 74% of races, the candidates with more "...
Methodology (1/2)<br />Sampling strategy<br />All the 23 provincial capitals;<br />229 candidates to major office;<br />3 ...
Methodology (2/2)<br />For each candidate<br />Candidate Prediction Gap = Candidate’s vote share - Facebook Likes shares<b...
Results (1/4)<br />18%<br />The winner was correctly predicted<br />39%<br />Other<br />43%<br />Most popular candidate on...
Results (2/4)<br />Correlazione di Pearson = 0,556<br />
Results (3/4)<br />
Results (4/4)<br />Correlazione di Pearson = 0,247<br />
Conclusions<br />The popularity of a candidate on Facebook is moderately correlated to the candidate’s vote share;<br />Ex...
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If Likes were votes: an empirical study on italian administrative election

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Presentazione al convegno PIC-AIS
Comunicazione e civic engagement. Istituzioni, cittadini e spazi pubblici nella postmodernità
22 e 23 Settembre, Università La Sapienza, Roma

Published in: News & Politics, Technology
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If Likes were votes: an empirical study on italian administrative election

  1. 1. IfLikeswerevotes…an empiricalstudy on Italianadministrativeelections<br />Fabio [.] Giglietto [@uniurb.it]<br />Department of CommunicationStudies| LaRiCA| Università di Urbino Carlo Bo<br />
  2. 2. Summary<br />Case presentation;<br />Research hypothesis;<br />Literature review;<br />Methodology;<br />Results;<br />Conclusions.<br />
  3. 3. Case presentation<br />Administrative election 15/16 May 2011<br />9 provinces;<br />1177 municipalities (23 provincial capital).<br />
  4. 4. Research hypothesis<br />H1: Is the popularity on Facebook a good predictor of a candidate electoral performance?<br />H2: How variables such as candidate’s political party and area, population of the municipality, number of candidates, number of candidates with Facebook page, number of potential voters and actual voters turn out affects the relationship between this two main variables.<br />
  5. 5. Literature review (1/2)<br />A correlation between Facebook Likes and votes share was found in 2006 midterm elections and in 2008 presidential primaries (Williams & Gulati,2007/2008/2009);<br />In 2009, Tumasjan, Sprenger, et alt. claimed that the number of messages mentioning a party reflected the election result of federal election of the national parliament in Germany (2010)… BUT;<br />
  6. 6. Literature review (2/2)<br />After 2010 midterm election Facebook claimed that in 74% of races, the candidates with more "likes" won. A more scientific study discovered some correlation for Senate but negative correlation for House (Trilogy Interactive, 2010);<br />A social election experiment run during Canadian election in 2011 claim that Facebook Likes can, in general, predict election results.<br />
  7. 7. Methodology (1/2)<br />Sampling strategy<br />All the 23 provincial capitals;<br />229 candidates to major office;<br />3 provincial capital with only one or less candidate on Facebook;<br />102 Facebook Pages (44,5%);<br />300.000+ Likes.<br />Data gathering<br />Facebook Graph API (snapshots from 25/04 to 15/05)<br />Ministero degli Interni e Regione Friuli Venezia Giulia<br />
  8. 8. Methodology (2/2)<br />For each candidate<br />Candidate Prediction Gap = Candidate’s vote share - Facebook Likes shares<br />For each municipality<br />Index of prediction accurateness (0-10);<br />Municipality Prediction Gap = AVERAGE(Candidate’s prediction Gap in the Municipality)<br />
  9. 9. Results (1/4)<br />18%<br />The winner was correctly predicted<br />39%<br />Other<br />43%<br />Most popular candidate on Facebook arrived second in the election<br />82%<br />
  10. 10. Results (2/4)<br />Correlazione di Pearson = 0,556<br />
  11. 11. Results (3/4)<br />
  12. 12. Results (4/4)<br />Correlazione di Pearson = 0,247<br />
  13. 13. Conclusions<br />The popularity of a candidate on Facebook is moderately correlated to the candidate’s vote share;<br />Extremist parties registered the highest prediction gap;<br />The prediction gap shrinks as the number of candidate on Facebook increase.<br />
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