Twitterology Research on Social Networks
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A talk given by Nicola Perra during the first Data Beers Torino on July 10, 2014.
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Twitterology Research on Social Networks
- 1. TWITTEROLOGY Nicola Perra www.nicolaperra.com
- 3. TWITTER’S FACTS 255 million users active (monthly)
- 4. TWITTER’S FACTS 255 million users active (monthly) 500 million tweets per day
- 5. TWITTER’S FACTS 255 million users active (monthly) 500 million tweets per day 78% of users are active on mobile devices
- 6. TWITTER’S FACTS 255 million users active (monthly) 500 million tweets per day 78% of users are active on mobile devices 77% of accounts are outside the U.S.
- 7. TWITTER’S FACTS 255 million users active (monthly) 500 million tweets per day 78% of users are active on mobile devices 77% of accounts are outside the U.S. 585 gallons (>2000 liters) of coffee per week
- 9. THE REAL QUESTION What can we do with it??!!
- 10. ANSWER QUESTIONS ABOUT US
- 11. ANSWER QUESTIONS ABOUT US How large is the circle of our friends?
- 12. ANSWER QUESTIONS ABOUT US How large is the circle of our friends? The social brain hypothesis:
- 13. ANSWER QUESTIONS ABOUT US How large is the circle of our friends? The social brain hypothesis: Typical social group size determined by neocortical size
- 14. ANSWER QUESTIONS ABOUT US How large is the circle of our friends? The social brain hypothesis: Typical social group size determined by neocortical size Measured in various primates, extrapolated for humans: 100-200 (Dunbar’s Number)
- 15. VALIDATION OF DUNBAR’S NUMBER IN TWITTER CONVERSATIONS By using 380 millions @ messages of about 1.7 millions users, we built the reciprocated weighted network A) B) 1 Alice 2 Bob 5 Cathy 3 Dan 4 Alice 6 Bob 7 Bob 10 Ellie 9 Cathy 11 Bob kin win wout A kin win wout kin win wout B C E D = 2 = 1 = 2 = 1 kout = 3 = 3 = 4 = 3 kout = 1 = 1 = 1 = 2 kout = 1 = 1 = 1 = 1 kin kout win wout = 0 = 1 = 0 = 1 kin kout win wout B. Goncalves, N. Perra, A. Vespignani, Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number, PLoS ONE 6(8), 2011
- 16. VALIDATION OF DUNBAR’S NUMBER IN TWITTER CONVERSATIONS 0 50 100 150 200 250 300 350 400 450 500 550 600 1 2 3 4 5 6 7 8 tout kout A) 50 100 150 200 250 300 350 400 450 500 550 600 0 50 100 150 0 l B) !out i = P j !ij kout i Average Weight per Connection B. Goncalves, N. Perra, A. Vespignani, Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number, PLoS ONE 6(8), 2011
- 17. VALIDATION OF DUNBAR’S NUMBER IN TWITTER CONVERSATIONS Average Weight per ConnectionNumber of connections for which interaction strength is highest 0 50 100 150 200 250 300 350 400 450 500 550 600 1 2 3 4 5 6 7 8 tout kout A) 50 100 150 200 250 300 350 400 450 500 550 600 0 50 100 150 0 l B) !out i = P j !ij kout i B. Goncalves, N. Perra, A. Vespignani, Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number, PLoS ONE 6(8), 2011
- 18. MAPPING THE USE OF LANGUAGES D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 19. MAPPING THE USE OF LANGUAGES 564 days of data collections (Twitter’s gardenhose) D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 20. MAPPING THE USE OF LANGUAGES 564 days of data collections (Twitter’s gardenhose) ~650 K Tweets/day with live GPS D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 21. MAPPING THE USE OF LANGUAGES 564 days of data collections (Twitter’s gardenhose) ~650 K Tweets/day with live GPS ~ 6 M of users D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 22. MAPPING THE USE OF LANGUAGES 564 days of data collections (Twitter’s gardenhose) ~650 K Tweets/day with live GPS ~ 6 M of users 191 countries (110 analyzed) D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 23. MAPPING THE USE OF LANGUAGES 564 days of data collections (Twitter’s gardenhose) ~650 K Tweets/day with live GPS ~ 6 M of users 191 countries (110 analyzed) Language detected 78 (Using Chromium) D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 24. MAPPING THE USE OF LANGUAGES D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 25. MAPPING THE USE OF LANGUAGES D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 26. MAPPING THE USE OF LANGUAGES D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 27. MAPPING THE USE OF LANGUAGES D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 28. MAPPING THE USE OF LANGUAGES D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 29. MAPPING THE USE OF LANGUAGES D. Mocanu, A. Baronchelli, N. Perra, B. Goncalves, A. Vespignani, The Twitter of Babel: Mapping World Languages through Microblogging Platforms, PLoS ONE, 8(4), 2013
- 34. PREDICT THE SEASONAL FLU
- 35. PREDICT THE SEASONAL FLU Two modeling techniques: fits VS generative models
- 36. PREDICT THE SEASONAL FLU Two modeling techniques: fits VS generative models How can we merge the two approaches?
- 37. PREDICT THE SEASONAL FLU Two modeling techniques: fits VS generative models How can we merge the two approaches?
- 38. PREDICT THE SEASONAL FLU Two modeling techniques: fits VS generative models How can we merge the two approaches?
- 39. PREDICT THE SEASONAL FLU
- 40. PREDICT THE SEASONAL FLU Extracting features of geographical locations, languages, and key words from Twitter, Google data, and ILI trend from CDC data. Generative models Calibrating generative models with multivariate fit. Stochastic simulations Inputs Parameters selection Forecasts STAGE 1 STAGE 2 STAGE 3 A A B B C C E D E D Twitter data Google data Multivariate fit Analyzing the forcasting results with CDC data in the past seasons Forecasting CDC
- 41. AND MORE…
- 42. AND MORE… Predicting the results of popular votes (American Idol). F. Ciulla et al, EPJ Data Science, 1, 8, 2012
- 43. AND MORE… Predicting the results of popular votes (American Idol). F. Ciulla et al, EPJ Data Science, 1, 8, 2012 Understanding human communications patterns (work in progress)
- 44. AND MORE… Predicting the results of popular votes (American Idol). F. Ciulla et al, EPJ Data Science, 1, 8, 2012 Understanding human communications patterns (work in progress) Understanding the spreading of # (work in progress)
- 45. AND MORE… Predicting the results of popular votes (American Idol). F. Ciulla et al, EPJ Data Science, 1, 8, 2012 Understanding human communications patterns (work in progress) Understanding the spreading of # (work in progress) Mapping cultural differences (work in progress)
- 46. CONCLUSIONS
- 47. CONCLUSIONS A lot has been done…
- 48. CONCLUSIONS A lot has been done… WHAT SHALL WE DO NEXT??
- 49. THANK YOU! n.perra@neu.edu www.nicolaperra.com