Case study: 2011 Spanish General Election (Twitter as a communication channel, as an opinion sensor, as a connection net, as a source of prediction)

1. 2011 Spanish
General Elections
2012, July 6 M. Luz Congosto / Pablo Aragón 1

2. SUMMARY
Twitter on Election campaign
 Status of art on Electoral prediction
Case study: 2011 Spanish General
Election
Conclusions / Findings
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3. Twitter on Election campaign
Comunication Opinion Sensor
Data Mining Prediction
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4. Status of art on Electoral prediction
 (Tumasjan, 2010) 2009 German Elections. Method count mentions
1,65% MAE (Mean Absolute Error)
 (Jungherr, 2011) 2009 German Elections
 (Conover D. , 2010) 2010 US Elections
 (Gayo-Avello D. , 2011) 2008 US elections
 (Tjong, 2012) 2011 Dutch Elections
 (Skoric, 2012) 2011 Singapur 2011
 (Bermingham et al., 2011) 2011 IIreland Elections
 (Panagiotis, 2011) 2010 US Elections
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5. Case study: 2011 Spanish General Election
 Methodology
 Twitter as a communication channel
 Twitter as an opinion sensor
 Twitter as a connection net
 Twitter as a source of prediction
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6. Case study: 2011 Spanish General Election
Methodology
– Dataset 1: Monitored tweets with mentions of national parties
from 08/10/11 to 22/11/11 using Twitter streaming API from
Carlos III University getting 2,973,110 tweets from 441,795
unique users
– Dataset 2: Stored tweets with mentions of political parties
represented in Parliament from 9-10-2011 to 24-11-2011 using a
routing process for downloading some users' timeline and
Twitter streaming API from Fundació Barcelona Media getting
2,279,250 tweets from 442,014 unique users
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7. Twitter as a communication channel
Candidate vs. Party
PSOE PP
Rajoy
Cayo
Rubalcaba Lara UpyD Equo
Treemap of followers before the campaing (Dataset-1)
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8. Twitter as a communication channel
Activity of campaign accounts on Twitter
Accumulated of tweets publish on campaign (Dataset-1)
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9. Twitter as a communication channel
Getting new followers
Accumulated of new followers on campaign(Dataset-1)
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10. Twitter as communication channel
Correlation new followers / unique mentions
Timeline (Dataset-1) Correlation by day (Dataset-1)
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11. Twitter as an opinion sensor
Citizen Participation
Tweets and users by day on campaign (Dataset-1)
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12. Twitter as an opinion sensor
Emotionality (valence)
Valence by day of campaign (Dataset-2)
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13. Twitter as an opinion sensor
Emotionality (dominance)
Dominance by day on campaign (Dataset-2)
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14. Twitter as an opinion sensor
Spread links
Europa
Política. El País Press ABC
El País
Público
El mundo
Treemap of mentions of Web sites on campaign (Dataset-1)
Inteactive image: http://barriblog.com/taller/javascript/protovis/sites_20N.html
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15. Twitter as a connection net
User Communities
Mapa of RTs between politicians on campaign (Dataset-2)
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16. Twitter as a source of prediction
Mentions vs. Results
Total mentions (name + @user + #hashtag) MAE=1,66%
Mentions count on campaign (Dataset-1)
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17. Twitter as a source of prediction
Political Polarity vs. Results
Total users MAE: 5,00%
Users with more than three polarity RTs or #hashtags on campaign (Dataset-1)
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18. Twitter as a source of prediction
Political Polarity vs. Results
Men(61,38%) MAE: 6,49% Women (38,62%) MAE: 3,88%
Users with more than three polarity RTs or #hashtags on campaign (Dataset-1)
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19. Conclusions / Findings
With measurements based on the mentions count we have
got a good result, however:
The results depend on many factors such as the social-cultural
environment in the elections, the period of the sample, campaign
events, the collection of data on Twitter, the parties analyzed
and calculation method
The validation of this method of forecasting requires
systemization of steps and checking of other elections
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20. Conclusions / Findings
With the measurements based on Political polarity we have
got worse results, we’ll have to bear this in mind to
improve the algorithms:
•Demographics: Twitter users are young and highly educated.
•Hidden opinion: Not all users show their political opinions
•Over opinion: Some parties supporters are very actives
•Entity vs. People: It’s difficult to distinguish an entity from a
person on Twitter
•Anonymous vs. “Real Identity”: users with a real identity are
more likely to have a hidden opinion
•Men vs. Women: There is a gender difference. Men are likely
to hide their opinion or to over opinion than women
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