From my talk at CWI in Amsterdam: http://bit.ly/2i65R4

1. David A. Shamma Internet Experiences Yahoo! Research A movie and a chat… http://www.flickr.com/photos/wvs/3833148925/

2. Internet Experiences GroupYahoo! Research (David) AymanShamma Lyndon Kennedy Elizabeth Churchill

3. Traditional Video

4. Traditional Comments and Tags Left in Whole, Unattached.

5. Some tags can be added as annotations Post annotations don’t allow for real time commentary. This part is interesting.

6. Social Conversations happen around videos Well – actually people join in a session and converse afterwards.

7. What to Collect to measure engagement? Type of event (Zync player command or a normal chat message) Anonymous hash (uniquely identifies the sender and the receiver, without exposing personal account data) URL to the shared video Timestamp for the event The player time(with respect to the specific video) at the point the event occurred The number of characters and the number words typed (for chat messages) Emoticons used in the chat message

8. A Short Movie

9. Percent of actions over time.

10. Volume of actions over time.

11. Chat follows the video! CHAT

12. Core Stats (opt-in) April 1, 2009 through April 7, 2009 (inclusive). 3.25 million events (URLs, chat, volumes, pause events). 24,258 users • 24,506 sessions Of these users, 35.29% (μ=2:02, σ= 2:72,σ2= 7:40) of the users engaged in more than one session during that week 76,762 URLs, 23% shared in more than one session. Over 99% of the shared videos came from YouTube. Approximately 2% of all the URLs sent within Yahoo! Messenger at-large.

13. Reciprocity 43.6% of the sessions the invitee played at least one video back to the session’s initiator. 77.7% sharing reciprocation Pairs of people often exchanged more than one set of videos in a session. In the categories of Nonprofit, Technology and Shows, the invitees shared more videos to the initiator (5:4, 9:7, and 5:2 respectably).

14. Social Actions and Live Performance DJs manage three social networks through group of mediums like: MySpace, Webcasts, Twitter, Facebook, and IM.

15. Y!Live

16. Y!Live

17. Y!Live

18. Much of Social Media is about Congregation Something we think about at CHI and CSCW and should think about at WWW and MM. (if you are so definition inclined you can enjoy the above paste)

19. A new form of indirect media-object annotation. 中国没有Twitter

20. People Tweet While They Watch

21. @kanye dude, not cool #vma Tweeting while watching offers implicit event annotation…one in need of media reification.

22. CurrentTV: Hack the Debate

23. a Tweet RT: @jowyang If you are watching the debate you’re invited to participate in #tweetdebate Here is the 411 http://tinyurl.com/3jdy67

24. Anatomy of a Tweet Repeated (retweet) content starts with RT Address other users with an @ RT: @jowyang If you are watching the debate you’re invited to participate in #tweetdebate Here is the 411 http://tinyurl.com/3jdy67 Rich Media embeds via links Tags start with #

25. Indirect Annotation Sept 26, 2009 18:23 EST RT: @jowyang If you are watching the debate you’re invited to participate in #tweetdebate Here is the 411 http://tinyurl.com/3jdy67

26. Tweet Crawl Three hashtags: #current #debate08 #tweetdebate 97 mins debate + 53 mins following = 2.5 hours total. 3,238 tweets from 1,160 people. 1,824 tweets from 647 people during the debate. 1,414 tweets from 738 people post debate. 577 @ mentions (reciprocity!) 266 mentions during the debate 311 afterwards. Low RT: 24 retweetsin total 6 during 18 afterwards.

27. Volume of Tweets by Minute Crawled from the Twitter RESTful search API.

28. Tweets During and After the Debates Conversation swells after the debate.

29. Volume of Conversation Follows the Debate Post debate

30. Does Conversation follow After a Segment Think of Isaac Newton Post Segment?

31. Will the roots of f’(x) find segmentation markers?

32. Automatic Segment Detection We use Newton’s Method to find extrema outside μ±σ to find candidate markers. Any marker that follows from the a marker on the previous minute is ignored.

33. Automatic Segment Detection with 92% Accuracy When compared to CSPAN’s editorialized Debate Summary ± 1 minute.

34. Tags As Boundary Objects

35. Directed Communication via @mentions John Tweets: “Hey @mary, my person is winning!” Makes a directed graph from John to Mary.

36. Barack, NewsHour, & McCain automatically discovered. High Eigenvector Centrality Figures on Twitter from the First US Presidential Debate of 2008.

37. Sinks in the network High in degree but poor centrality.

38. Tweets to Terms Common stems in bold-italic.

39. Tweets are Reaction not Content

40. What if we look at other events? What can we find?

41. Argentina v England (1986 FIFA World Cup quarter-final)

42. Nooo! #worldcup #hand Event Onset!

43. July 20, 1969 Apollo 11 Moon Landing

44. omg! @nasarukddng? #landing #fake #moon Tags as boundary objects can find communities & sets.

45. Godzilla attacking the Tokyo

46. やばい！国会議事堂をつぶしている！@radonがんばって！#gojira Tweet Content Comprehension Need Not Be Needed.

47. Inauguration 2009 http://www.flickr.com/photos/twistedart/3212723019/

48. Data Mining Feed 53,000 Tweets @ 600 per minutes

49. Drop in @conversation as onset

50. Drop in @conversation as onset (inaug subset)

51. Less @ means less chars

52. Terms as topics Using a TF/IDF window of 5 mins

53. Sustained Interest Some topics continue over time with a higher conversational context.

54. People Announce (12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human! (12:07) ryantherobot: LOL Obama messed up his inaugural oath twice! regardless, Obama is the president today! whoooo! (12:46) mattycus: RT @deelah: it wasn’t Obama that messed the oath, it was Chief Justice Roberts: http://is.gd/gAVo (12:53) dawngoldberg: @therichbrooks He flubbed the oath because Chief Justice screwed up the order of the words.

55. People Reply (12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human! (12:07) ryantherobot: LOL Obama messed up his inaugural oath twice! regardless, Obama is the president today! whoooo! (12:46) mattycus: RT @deelah: it wasn’t Obama that messed the oath, it was Chief Justice Roberts: http://is.gd/gAVo (12:53) dawngoldberg: @therichbrooks He flubbed the oath because Chief Justice screwed up the order of the words.

56. HCC and MM and Other Work Indirect annotation through community action Uncollected Sources (read: events) are highly valuable Segmentation, Figure Identification, Term Distance Bigrams? Real Time?

57. Statler http://bit.ly/statler

58. Thanks Chloe S., Ben C., Marc S., M. Cameron J., Ryan S., & NodeXL Tweet the Debates: Understanding Community Annotation of Uncollected Sources David A. Shamma; Lyndon Kennedy; Elizabeth F. Churchill, ACM Multimedia, ACM, 2009 Understanding the Creative Conversation: Modeling to Engagement David A. Shamma; Dan Perkel; Kurt Luther, Creativity and Cognition, ACM, 2009 Spinning Online: A Case Study of Internet Broadcasting by DJs David A. Shamma; Elizabeth Churchill; Nikhil Bobb; Matt Fukuda, Communities & Technology, ACM, 2009 Zync with Me: Synchronized Sharing of Video through Instant Messaging David A. Shamma; Yiming Liu; Pablo Cesar, David Geerts, KonstantinosChorianopoulos, Social Interactive Television: Immersive Shared Experiences and Perspectives, Information Science Reference, IGI Global, 2009 Enhancing online personal connections through the synchronized sharing of online video Shamma, D. A.; Bastéa-Forte, M.; Joubert, N.; Liu, Y., Human Factors in Computing Systems (CHI), ACM, 2008 Supporting creative acts beyond dissemination David A. Shamma; Ryan Shaw, Creativity and Cognition, ACM, 2007 Watch what I watch: using community activity to understand content David A. Shamma; Ryan Shaw; Peter Shafton; Yiming Liu, ACM Multimedia Workshop on Multimedia Information Retrival (MIR), ACM, 2007 Zync: the design of synchronized video sharing Yiming Liu; David A. Shamma; Peter Shafton; Jeannie Yang, Designing for User eXperiences, ACM, 2007