The Social World of Twitter: Topics, Geography, and Emotions

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The social world of twitter
http://tinyurl.com/7xdv524

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  • The Social World of Twitter: Topics, Geography, and Emotions

    1. 1. The Social World of Twitter:Topics, Geography, and Emotions@danielequercia
    2. 2. <who am i>
    3. 3. daniele quercia
    4. 4. offline & online
    5. 5. <goal>
    6. 6. social media language personality social media
    7. 7. social media <why>
    8. 8. social media
    9. 9. Pop press pundits (Archbishop England&Walses) social media“Social-networking sites “dehumanize” community life”
    10. 10. social media
    11. 11. social media 1 Q&A
    12. 12. social media 2 Q&A
    13. 13. social media 3 Q&A
    14. 14. CS Researchers:“Twitter is NOT media social a social network but a news media”
    15. 15. Pop press pundits (Archbishop England&Wales): social media“Social-networking sites “dehumanize” community life”CS Researchers:“Twitter is NOT a social network but a news media”
    16. 16. Pop press pundits (Archbishop England&Wales) social media“Social-networking sites “dehumanize” community life”CS Researchers:“Twitter is NOT a social network but a news media” er” ;-) g to diff “I be
    17. 17. social media language personality social media
    18. 18. Goal: Characterize Twitter ``community’’ 1 collect profiles 2 compute (ego)network metrics 3 relate metrics to 3 aspects
    19. 19. 1 collect profiles 3 seeds: newspaper accounts 250K profiles in London (31.5M tweets) 228K profiles
    20. 20. 2 compute (ego)network metrics 228K egonetworks 4 versions: original, reciprocal(24%), 1-way msg(4%), 2-way(<1%)
    21. 21. 3 relate net metrics to 3 aspects a Topics b Geography c Emotions
    22. 22. a topics hp 1: higher diversity – higher brokerage Get topics & Compute diversity AlchemyAPI, OpenCalais, TextWise
    23. 23. a topics hp 1: higher diversity – higher brokerage Get topics & Compute diversity AlchemyAPI, OpenCalais, TextWise
    24. 24. a topics hp 1: higher diversity – higher brokerage
    25. 25. a topics hp 1: higher diversity – higher brokerage
    26. 26. a topics hp 1: higher diversity – higher brokerage
    27. 27. b geographyhp 2: closely-knit - less geo dispersed
    28. 28. b geographyhp 2: closely-knit - less geo dispersed
    29. 29. b geographyhp 2: closely-knit - less geo dispersed
    30. 30. b geographyhp 2: closely-knit - less geo dispersed
    31. 31. c emotionshp 3: closely-knit – emotion sharing
    32. 32. c emotionshp 3: closely-knit – emotion sharing
    33. 33. c emotionshp 3: closely-knit – emotion sharing
    34. 34. c emotionshp 3’: homophily
    35. 35. 1. Brokers tend to cover diverse topics2. Users have a “typical” geo span3. “Happy” (“sad”) users do cluster together
    36. 36. Future (well, current & you could help)
    37. 37. 1 complex buildings
    38. 38. “Who talks to whom”
    39. 39. Network
    40. 40. 2 tools for topical & sentiment analysis
    41. 41. social mediaenvironmentsportshealth wedding parties Spanish/Portuguese celebrity gossips
    42. 42. Support Vector Regression IMD <- SVR(topics) accuracy: 8.14 in [13.12,46.88]
    43. 43. 3
    44. 44. 3
    45. 45. 1 Complex Buildings2 Tools for topical & sentiment analysis3 urbanopticon.org
    46. 46. @danielequercia

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