Presented during the 10th International Conference of Sociocybernetics, Cracow, Poland, 20-25 June 2011

1. On user generated content, teleology and predictability in social systems Fabio [.] Giglietto [@uniurb.it] Deparment of CommunicationStudies| LaRiCA| Università di Urbino Carlo Bo 10th International Conference of Sociocybernetics, Cracow, Poland, 20-25 June 2011

2. Summary Purposefulness and Teleology; What Society Wants?; Case Study: FacebookLikes & Local Elections in Italy.

3. Purposefulness and Teleology Arturo Rosenblueth, Norbert Wiener and Julian Bigelow Behavior, Purpose and Teleology in: Philosophy of Science, 10(1943), S. 18–24

4. What Society Wants? Parsons: A.G.I.L. functional imperatives; Luhmann: Evolution of the structure of society; Improbability of communication; Language; Distribution media (time/space); Symbolically generalized communication media. Semantics.

5. Impact of the Internet on the evolution of distribution media

6. Effects of the Internet Increased number of permanent, searchable communications -> effect on the probability of communication; Society may observe itself as never before (Google Books Ngram Viewer); Can we predict the behavior of social systems by analyzing the patterns of previous communications?

7. Literature review PredictingSearch Trends, Digg and YouTubevideo popularity, Unemployment Rate (US, Germany); Google Flu Trends; Previous study on Facebook and Elections (1, 2, 3).

8. Case study: predicting Italian local elections with Facebook Case 15th and 16th of May; More than 3000 municipalities involved. Methodology Data collection Google Spreadsheet (platform for collaboration & sharing); Manual search and data entry of candidates and their official Facebook Pages; Google Script to automatically retrieve and save the number of Likes.

9. Case study: predictingItalianlocalelections with Facebook Sample 29 municipalities*, 229 major candidates; 102 FacebookPages (44,5% of candidates); Over 300.000 totalLikes. * All the provincial capitals

11. Case study: predictingItalianlocalelections with Facebook Analysis Index of Predictionaccurateness [on a scale 0 to 10]; ∆ =𝑉𝑜𝑡𝑒𝑠 𝑆h𝑎𝑟𝑒 - 𝐹𝑎𝑐𝑒𝑏𝑜𝑜𝑘 𝐿𝑖𝑘𝑒𝑠 𝑆h𝑎𝑟𝑒 (∆ calculated by candidate, by municipality and by political areas).

12. Case study: predictingItalianlocalelections with Facebook Results 18% The winner was correctly predicted 39% Other 43% Most popular candidate on Facebook arrived second in the election 82%

13. Case study: predictingItalianlocalelections with Facebook Results Index of predictionaccurateness averagescore: 4,7

14. Case study: predictingItalianlocalelections with Facebook Results AverageMunicipality∆ =𝐴𝐵𝑆(22%)

15. Conclusions Predicting the semantics (based on past ) in social systemsappears to be promising; The accurateness of prediction is correlated to the quantity of data available; Emergence of new feedback loops.