The document discusses research analyzing the social world of Twitter. It aims to characterize Twitter communities by collecting profiles, computing network metrics, and relating metrics to topics, geography, and emotions. The research finds that (1) users who broker diverse topics tend to have higher network diversity, (2) users typically have a geo span, and (3) "happy" and "sad" users tend to cluster together emotionally. Future work involves analyzing complex networks, improving tools for topic and sentiment analysis, and the urbanopticon.org project.

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