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

1. The Social World of Twitter:
Topics, Geography, and Emotions
@danielequercia

2. <who am i>

3. daniele quercia

10. offline & online

11. <goal>

12. social media
language personality
social media

13. social media
<why>

14. social media

15. Pop press pundits (Archbishop England&Walses)
social media
“Social-networking sites “dehumanize” community life”

16. social media

17. social media
1
Q&A

18. social media
2
Q&A

19. social media
3
Q&A

20. CS Researchers:
“Twitter is NOT media
social a social network but a news media”

21. 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”

22. 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

23. social media
language personality
social media

24. Goal: Characterize Twitter ``community’’
1 collect profiles
2 compute (ego)network metrics
3 relate metrics to 3 aspects

25. 1 collect profiles
3 seeds: newspaper accounts
250K profiles in London (31.5M tweets)
228K profiles

26. 2 compute (ego)network metrics
228K egonetworks
4 versions: original, reciprocal(24%),
1-way msg(4%), 2-way(<1%)

27. 3 relate net metrics to 3 aspects
a Topics
b Geography
c Emotions

28. a topics
hp 1: higher diversity – higher brokerage
Get topics & Compute diversity
AlchemyAPI, OpenCalais, TextWise

29. a topics
hp 1: higher diversity – higher brokerage
Get topics & Compute diversity
AlchemyAPI, OpenCalais, TextWise

30. a topics
hp 1: higher diversity – higher brokerage

31. a topics
hp 1: higher diversity – higher brokerage

32. a topics
hp 1: higher diversity – higher brokerage

33. b geography
hp 2: closely-knit - less geo dispersed

34. b geography
hp 2: closely-knit - less geo dispersed

35. b geography
hp 2: closely-knit - less geo dispersed

36. b geography
hp 2: closely-knit - less geo dispersed

37. c emotions
hp 3: closely-knit – emotion sharing

38. c emotions
hp 3: closely-knit – emotion sharing

39. c emotions
hp 3: closely-knit – emotion sharing

40. c emotions
hp 3’: homophily

41. 1. Brokers tend to cover diverse topics
2. Users have a “typical” geo span
3. “Happy” (“sad”) users do cluster together

42. Future (well, current & you could help)

43. 1 complex buildings

44. “Who talks to whom”

45. Network

46. 2 tools for topical & sentiment analysis

47. social media
environment
sports
health wedding parties
Spanish/Portuguese
celebrity gossips

48. Support Vector Regression
IMD <- SVR(topics)
accuracy: 8.14 in [13.12,46.88]

49. 3

50. 3

51. 1 Complex Buildings
2 Tools for topical & sentiment analysis
3 urbanopticon.org

52. @danielequercia