1. TWITTER What is Twitter, a Social Network or a News Media? Haewoon Kwak Changhyun Lee Hosung Park Sue Moon Department of Computer Science, KAIST, Korea 19th International World Wide Web Conference (WWW2010)

2. TWITTER 2 Twitter, a microblog service write a short message

3. TWITTER 3 Twitter, a microblog service read neighbors’ tweets

4. TWITTER 4 In most OSN “We are friends.”

5. TWITTER 5 In Twitter “I follow you.”

6. TWITTER 6 Following on Twitter “Unlike most social networks, following on Twitter is not mutual. Someone who thinks you're interesting can follow you, and you don't have to approve, or follow back." http://help.twitter.com/entries/14019-what-is-following 6

7. TWITTER 7 Following = subscribing tweets recent tweets of followings

8. 8

9. TWITTER 9 http://blog.marsdencartoons.com/2009/06/18/cartoon-iranian-election-demonstrations-and-twitter/marsden-iran-twitter72/

10. 10 The goal of this work We analyze how directed relations of following set Twitter apart from existing OSNs. Then, we see if Twitter has any characteristics of news media. PROBLEM STATEMENT

11. TWITTER 11 me ⋅ di ⋅ a [mee-dee-uh] 1. a pl. of medium 2. the means of communication, as radio and television, newspapers, and magazines, that reach or influence people widely http://dictionary.reference.com/

12. 12 The goal of this work We analyze how directed relations of following set Twitter apart from existing OSNs. Then, we see if Twitter has any characteristics of news media. PROBLEM STATEMENT

13. 13 Summary of our findings 1. Following is mostly not reciprocated (not so “social”) 2. Users talk about timely topics 3. A few users reach large audience directly 4. Most users can reach large audience by Word of Mouth (WOM) quickly OUTLINE

14. TWITTER 14 Data collection (2009/06/01~09/24) 41.7M user profiles (near-complete at that time) 1.47B following relations 4262 trending topics 106M tweets mentioning trending topics ‣ Spam tweets removed by CleanTweets *http://an.kaist.ac.kr/traces/WWW2010.htmlhttp://an.kaist.ac.kr/traces/WWW2010.html

15. TWITTER 15 How we crawled Twitter’s well-defined 3rd party API With 20+ ‘whitelisted’ IPs ‣ Send total 20,000 requests per IP / hour

16. TWITTER 16 Recent studies Ranking methodologies [WSDM’10] Predicting movie profits [HYPERTEXT’10] Recommending users [CHI’10 microblogging] Detecting real time events [WWW’10] The ‘entire’ Twittersphere unexplored

17. TRANSITION 17 Part I. 1. Following is mostly not reciprocated (not so “social”) 2. Users talk about timely topics 3. A few users reach large audience directly 4. Most users can reach large audience by WOM* quickly

18. 2. ACTIVE SUBSCRIPTION 18 Why do people follow others? Reflection of offline social relationships Subscription to others’ messages otherwise,

19. 2. ACTIVE SUBSCRIPTION 19 Sociologists’ answer “Reciprocal interactions pervade every relation of primitive life and in all social systems”

20. 2. ACTIVE SUBSCRIPTION 20 Is following reciprocal? Only 22.1% of user pairs follow each other Much lower than ‣ 68% on Flickr ‣ 84% on Yahoo! 360 ‣ 77% on Cyworld guestbook messages

21. 2. ACTIVE SUBSCRIPTION 21 Low reciprocity of following Following is not similarly used as friend in OSNs ‣ Not really reflection of offline social relationships Active subscription of tweets!

22. TRANSITION 22 Part II. 1. Following is mostly not reciprocated (not so “social”) 2. Users talk about timely topics 3. A few users reach large audience directly 4. Most users can reach large audience by WOM* quickly

23. 1. TIMELINESS TOPICS 23 Dynamically changing trends

24. 1. TIMELINESS TOPICS 24 User participation pattern can be a signature of a topic

25. 1. TIMELINESS TOPICS 25 Majority of topics are headline 54.3% “headline news” 31.5% “ephemeral” 6.9% 7.3% “persistent news”

26. TRANSITION 26 Part III. 1. Following is mostly not reciprocated (not so “social”) 2. Users talk about timely topics 3. A few users reach large audience directly 4. Most users can reach large audience by WOM* quickly

27. 3. A FEW HUBS 27 How many followers a user has?

28. 3. A FEW HUBS P(x)dx 28 CCDF Complementary Cumulative Density Function CCDF(x=k) =

29. 3. A FEW HUBS 29 Reading the graph

30. 3. A FEW HUBS 30 Plenty of super-hubs

31. 3. A FEW HUBS 31 More super-hubs than projected by power-law Where do they get all the followers? Possibly from... ‣ Search by ‘name’ ‣ Recommendation by Twitter They reach millions in one hop

32. 3. A FEW HUBS 32 Are those who have many followers active?

33. 3. A FEW HUBS 33 How we plotted

34. 3. A FEW HUBS 34 How we plotted Avg. = 8 × Med. = 9

35. 3. A FEW HUBS 35 More followers, more tweets

36. 3. A FEW HUBS 36 Many followers without activity

37. 3. A FEW HUBS 37 Twitter user rankings by Followers, PageRank and RT

38. 3. A FEW HUBS 38 Twitter user rankings by Followers, PageRank and RT

39. 3. A FEW HUBS 39 Great discrepancy among rankings Kandal’s tau rank correlation Top k ranking # followers (RF), PageRank (RPR) # retweets (RRT )

40. TRANSITION 40 Part IV. 1. Following is mostly not reciprocated (not so “social”) 2. Users talk about timely topics 3. A few users reach large audience directly 4. Most users can reach large audience by WOM* quickly 5. *WOM: word-of-mouth

41. 4. WORD-OF-MOUTH 41 Which is more efficient for WOM?

42. 4. WORD-OF-MOUTH 42 In Twitter Following Information

43. 4. WORD-OF-MOUTH 43 Average path length: 4.1

44. 4. WORD-OF-MOUTH 44 Retweet (RT) Relay tweets from a following to followers

45. 4. WORD-OF-MOUTH 45 Retweet (RT) Relay tweets from a following to followers Last day of WWW’10 node 0

46. 4. WORD-OF-MOUTH 46 Retweet (RT) Relay tweets from a following to followers Last day of WWW’10

47. 4. WORD-OF-MOUTH 47 Retweet (RT) Relay tweets from a following to followers RT @node0 Last day of WWW’10 node 4

48. 4. WORD-OF-MOUTH 48 Retweet (RT) Relay tweets from a following to followers RT @node0 Last day of WWW’10

49. 4. WORD-OF-MOUTH 49 Retweet (RT) Relay tweets from a following to followers writer retweeter w r

50. 4. WORD-OF-MOUTH 50 Retweet (RT) Not only 1 hop neighbors 1 hop neighbors w r

51. 4. WORD-OF-MOUTH 51 Retweet (RT) More goes further 2 hop neighbors 51 w r

52. 4. WORD-OF-MOUTH 52 We construct RT tree A tree with writer and retweeter(s) w r

53. 4. WORD-OF-MOUTH 53 Height of RT trees r W r W rr W rr r 1 12

54. 4. WORD-OF-MOUTH 54 Empirical RT trees

55. 4. WORD-OF-MOUTH 55 96% of RT trees = Height 1

56. 4. WORD-OF-MOUTH 56 Boosting audience by RT

57. 4. WORD-OF-MOUTH 57 Additional readers 3 followers 2 additional readers by retweeter w r

58. 4. WORD-OF-MOUTH 58 A retweet brings a few hundred additional readers

59. 4. WORD-OF-MOUTH 59 Time lag between hops in RT tree

60. 4. WORD-OF-MOUTH 60 Fast relaying tweets by RT: 35% of RT < 10 min.

61. 4. WORD-OF-MOUTH 61 Fast relaying tweets by RT: 55% of RT < 1hr.

62. SUMMARY 62 Summary 1. We study the entire Twittersphere 2. Low reciprocity distinguishes Twitter from OSNs 3. Twitter has characteristics of news media: ‣ Tweets mentioning timely topics ‣ Plenty of hubs reaching a large public directly ‣ Fast and wide spread of word-of-mouth